Topics

<> machine learning (96)

Remote Sensing and Agroinformatics Insights in Saudi Arabia Using Machine Learning

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B9 L2 R2325

This talk explores how machine learning and high-resolution satellite remote sensing are being used to transform vast amounts of raw data into actionable agroinformatics at a national scale, providing the precision needed to manage these vital resources sustainably.

Ting Li

Postdoctoral Research Fellow, Environmental Science and Engineering
Ting Li is a Postdoctoral Research Fellow focusing her research on bridging the gap between complex data and real-world agricultural impact.

Yanda Meng

Assistant Professor, Bioengineering
Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective

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B1 L3 R3119

This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.

KAUST, in collaboration with MCIT, to host the second edition of the MENA Machine Learning Winter School (MenaML)

1 min read ·
A week of advanced instruction and exchange for emerging machine learning talent.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Racha Zayni

Visiting Researcher, Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal Aldossary is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

KAUST advances scalable AI through global collaboration

1 min read ·
KAUST brings global experts together to advance scalable AI and tackle the challenges of training large models.

Stochastic Interacting Particle Methods and Generative Learning for Multiscale PDEs

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B1 L3 R3119

This talk discusses stochastic interacting particle (SIP) methods for advection-diffusion-reaction PDEs based on probabilistic representations of solutions, and shows their self-adaptivity and efficiency in several space dimensions.

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Jonathan Klein

Research Scientist, Computer Science
My fields of research interests include computational simulation, plant simulation and modeling, synthetic training data, machine learning architectures, none-line-of-sight imaging, and computational photography.

AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory

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B9 L3 R3120

This talk describes a novel machine learning algorithm, inspired by complex analysis and dynamical systems theory, that significantly improves efficiency in solving partial differential equations and scientific computing, while providing a theoretical framework for reconstructing complex, unknown systems from partial observational data.

KAUST Workshop on Distributed Training in the Era of Large Models

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Auditorium between B4 & 5, L0, R0215

Join leading researchers and innovators to explore how distributed training is reshaping the next generation of large-scale AI models.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

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B9 L2 R2325

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

AI for Science: AI-Driven Knowledge Discovery

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B1 L3 R3119

This talk focuses on the latest progress in this area, often called AI for Science, and specifically on how AI is revolutionizing scientific exploration and the uncovering of fundamental physical laws.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Yibin Wang

Keep Moving forward

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Randomized Greedy Algorithms for Neural Network Optimization in Solving Partial Differential Equations

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B4 L5 R5220

This thesis introduces the randomized orthogonal greedy algorithm (ROGA) to bridge the gap between theoretical and practical performance of shallow neural networks for solving partial differential equations by overcoming key optimization challenges to achieve provably optimal convergence rates.

Andrea Vanessa Rocha

Research Specialist, Statistics

Vecchia Approximations of Gaussian Processes on GPUs for Scalable Spatial Modeling and Computer Model Emulation

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B9 L2 R2325

This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Some Contributions to Particle and Unbiased Simulation Methods

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This thesis develops unbiased estimators and efficient particle filters, leveraging Feynman-Kac formulae and multilevel techniques, to address challenges in parameter estimation, filtering, invariant measure approximation, and stationary distribution computation for partially observed diffusion processes and McKean-Vlasov stochastic differential equations.

On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning

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B9 L2 R2325

This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning.
Stochastic Numerics and Statistical Learning Workshop

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

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Auditorium 0215 between B2 & B3

Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.
Decision Trees Versus Systems of Decision Rules

New book by KAUST Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov and Professor Mikhail Moshkov published by Springer

1 min read ·
New book by Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov, and Professor Mikhail Moshkov explores decision trees and rule systems using rough set theory.

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

MS Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Dynamics and Convergence of Weight Normalization for Training Neural Networks

Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

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B1 L3 R3119

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

On Recently Developed Non-Gaussian Priors and Sampling Methods with Application to Industrial Tomography

Prof. Lassi Roininen, Applied Mathematics, LUT University

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B1 L4 R4214

We consider two sets of new priors for Bayesian inversion and machine learning: The first one is based on mixture of experts models with Gaussian processes. The target is to estimate the number of experts and their parameters, and to make state estimation. For sampling, we use SMC^2. For non-Gaussian priors, we discuss Cauchy priors and the generalisation to high-order Cauchy fields and further generalisation to alpha-stable fields. For sampling, we use a selection of modern MCMC tools. Finally, we apply some of the methods and models to an industrial tomography problem on estimating log internal structure, measured at sawmills, based on X-ray, RGB camera and laser scanning.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

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B9

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

Universal light encoders: artificial intelligent optical hardware for real-time hyperspectral imaging and ultrasensitive detection

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B9 L2 H2

In this talk, I will review the fundamentals and application of this technology in various areas, ranging from the inverse design of ultra-flat optical components to HyplexTM, an innovative camera for acquiring and processing high-resolution hyperspectral videos in real-time at 30 frames per second.

Adaptive Optimization Algorithms for Machine Learning

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B1 L4 R4214

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.

Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

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B3 L5 R5209

Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

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B2 B3 A0215

We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Meet KAUST Prospective Student: Igor Sokolov

1 min read ·
Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST student: Grigory Malinovsky

1 min read ·
Grigory Malinovsky joined KAUST this fall as a M.S./Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab. Grigory holds a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Malinovsky first came to KAUST as a visiting student and was instantly impressed by the research facilities and working atmosphere. His outstanding internship experience was a catalyst in choosing KAUST as the next step in his academic career.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

Deeper defense against cyber attacks

1 min read ·
To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Xun Qian

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected

Waqas Waseem Ahmed

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Erasmus Mundus Joint Doctorate in Photonics Engineering, Universitat Politècnica de Catalunya (Spain), Universita degli Studi di Firenze (Italy), Karlsruhe Institute of Technology (Germany), Université Paul Cèzanne Aix Marseille III (France).(2018) MS in Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. (2014) BS in Electronic Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI), Pakistan. (2009) Research Interests Non-Hermitian Photonics, Nonlinear Optics, Machine Learning, Laser Physics.

Slavomir Hanzely

Slavomir Hanzely is an MS/Ph.D. student in the Applied Mathematics and Computational Science Program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Slavomir earned his Bachelor's Degree in Informatics with a background in both Mathematics and Computer Science from the Faculty of Mathematics, Physics and Informatics University (FMPH UK), Slovakia in 2019. Research Interest He is interested in Machine Learning, Federated Learning, and Stochastic Algorithms. Education
Sameh Abdulah

Sameh Abdulah

Dr. Sameh Abdulah specializes in HPC, scalable algorithms, and climate modeling, with innovations receiving the ACM Gordon Bell Prize for Climate Modelling.
Sabine El Khoury

Sabine El Khoury

Sabine El Khoury is an instructional professor of applied mathematics and computational sciences specializing in commutative algebra.

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Mikhail Moshkov

Professor Moshkov specializes in various areas of discrete applied mathematics, theoretical computer science and operations research. He is the author or co-author of more than 200 research publications, including 10 books published by Springer.

Mher Safaryan

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Mher Safaryan is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mher received his Ph.D. in Mathematics from Yerevan State University, Armenia under the supervision of Prof. Grigori Karagulyan. Before joining KAUST, he was a Junior Researcher at the Institute of Mathematics of National Academy of Sciences, Real Analysis Department in Armenia. After that, he joined the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)

Juncai He

I am currently a research scientist in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received a B.S. degree in Mathematics and Applied Mathematics from Sichuan University in 2014. In the Summer of 2019, I received my Ph.D. degree in Computational Mathematics under the supervision of Prof. Jinchao Xu and Prof. Jun Hu at Peking University in Beijing, China. From 2019 to 2020, I worked as a Postdoctoral Scholar supervised by Prof. Jinchao Xu in The Center for Computational Mathematics and Application

Jorge Alejandro Amador Herrera

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic

Jongho Park

Jongho Park is a Research Scientist at King Abdullah University of Science and Technology (KAUST) where his research focuses on the design and analysis of efficient numerical methods for variational problems.

Irving Enrique Reyna Nolasco

Research Interests Irving Enrique Reyna Nolasco's research interests include numerical methods for partial differential equations, large eddy simulations, high order entropy stable schemes, machine learning, complex systems, and non-linear dynamics. Why did you choose your field of research? As a graduate student, I have always found satisfaction in developing scientific software and libraries for the simulation and visualization of different phenomena. I enjoy applying scientific computing tools and mathematical concepts to relevant problems in physics. By the end of my bachelor studies, I

Igor Sokolov

Igor Sokolov is an M.S. candidate in the KAUST Peter Richtarik Research Group under the supervision of Professor Peter Richtarik. Before joining KAUST, Igor obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Igor's research interests lie in the intersection of optimization and machine learning. He is interested in both the theoretical analysis of optimization algorithms as well as their practically efficient applications.

Igor Chikalov

Senior Research Scientist, Computer Science
Research Interests Application of dynamic programming to building decision trees and decision rules Machine learning Design of algorithms and complexity analysis Selected Publications I. Chikalov, V. Lozin, I. Lozina, M. Moshkov, H.S. Nguyen, A. Skowron, B. Zielosko, Three Approaches to Data Analysis: Test Theory, Rough Sets and Logical Analysis of Data, Intelligent Systems Reference Library, Vol. 41, Springer, 2012 I. Chikalov, Average Time Complexity of Decision Trees, Intelligent Systems Reference Library, Vol. 21, Springer, 2011. I. Chikalov, P. Yao, M. Moshkov, J.C. Latombe, Learning

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied

Grigorii Malinovskii

Ph.D. Student, Computer Science
I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.

Filip Hanzely

Filip Hanzely is a recent PhD graduate from the group of Peter Richtarik. His research focuses mostly on various aspects of optimization for machine learning, designing provably efficient algorithms for solving big data problems. Filip is joining Toyota Technological Institute in Chicago (TTIC) as a research assistant professor in December 2020. Education and Early Career Filip Hanzely graduated with honor for his bachelor degree in Economics and Financial Mathematics from Comenius University in Bratislava, Slovakia. Later on in 2017, he received his master in science degree in Mathematics and

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Aritra Dutta

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Aritra Dutta is a Postdoctoral Research Fellow at the Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Panagiotis Kalnis in his research group. Prior to that, he was a Postdoctoral Research Fellow in the Optimization and Machine Learning group of Professor Peter Richtárik. Education and Early Career Aritra Dutta received his bachelor degree in Mathematics (Hons.), Minor in Physics and Statistics from Presidency College in Calcutta University. He obtained a master degree in Mathematics and Computing from Indian

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Aku-Jaakko Alexis Kammonen

Aku-Jaakko Alexis Kammonen is a Research Scientist at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST).
<> statistics (68)

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).
Juan Jose Galeano Arenas

Juan José Galeano Arenas

Ph.D. Student, Statistics
Juan José Galeano Arenas is a MS/PhD student at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group.

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Spatio-Temporal Data Analysis by Transformed Gaussian Processes

Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

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B2 L5 R5220

In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Statistical models based on stochastic partial differential equations

Prof. David Bolin, Statistics, KAUST

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B9 L2 H1 R2322

The talk will give an overview of some recent developments of statistical models based on stochastic partial differential equations. We will in particular focus on equations with non-local differential operators or non-Gaussian driving noise, and explain when any why such models are useful. As motivating applications, analysis of longitudinal medical data and ocean waves will be considered.

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

1 min read ·
Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

Brain waves in boxes

1 min read ·
A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

1 min read ·
The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Feeding in the moonlight

1 min read ·
Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

A clearer view of rainfall patterns

1 min read ·
Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

1 min read ·
By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

1 min read ·
A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

Getting the jump on industrial failures

1 min read ·
A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Keeping antennas at peak performance

1 min read ·
Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

A safe sweep of weather data

1 min read ·
A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Smart healthcare uses stats to spot a stumble

1 min read ·
Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Going to extremes to predict natural disasters

1 min read ·
A systematic approach to selecting and configuring statistical models improves predictions of extreme events.

Letting the data speak for itself

1 min read ·
A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Dataset size counts for better predictions

1 min read ·
A statistical technique for modeling large datasets improves interpretation of climate and environmental data.

Diagnosing the impacts of health policy

1 min read ·
Study design allows more rigorous evaluation of complex healthcare interventions.

One bad apple can spoil a robot swarm

1 min read ·
Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Modeling where the wind blows

1 min read ·
A new statistical technique could be used to more accurately locate global wind resources.

Revealing hidden relationships in data

1 min read ·
A method to visualize hidden statistical structure helps make sense of environmental data.

Making data more accessible

1 min read ·
Ying Sun is a multi-award-winning statistician who is inspired by the value of statistics in solving real-world problems.

Pulling rank on spatial statistics

1 min read ·
A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

Challenging statistics of weather extremes

1 min read ·
More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Zooming in on brain function

1 min read ·
Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Second year Ph.D. student to receive top statistics award

1 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Connecting the dots to unlock brain structure

1 min read ·
Mapping genetic influences on connections between neural networks could lead to a better understanding of brain organization and behavior.

Making loudspeakers resonate like atoms

1 min read ·
Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

Searching for wind for the future

1 min read ·
The first quantification of wind energy in Saudi Arabia points to high wind power potential for many decades to come.

Reining in computational complexity

1 min read ·
A more efficient approach to modeling spatial data involving thousands of variables keeps computation time in check.

Solving real-world problems

1 min read ·
A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

Stimulating deeper insights into brain function

1 min read ·
Modeling changes in brain activity over time provides deeper insights into learning and behavioral responses.

New stats apps show a virtual reality

1 min read ·
Harnessing the power of virtual reality will help to visualize data and improve statistical models.

Clustering helps unlock secrets of the human brain

1 min read ·
The latest statistical methods from research on complex high-dimensional environmental data also yield powerful tools for interpreting brain activity.

Simple statistics can be good enough

1 min read ·
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Taking aim at outliers

1 min read ·
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Epilepsy study shows the shape of things to come

1 min read ·
Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

Honing in on hotspots in spatial data

1 min read ·
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

Marc Genton received an ISI Service Award 2019

1 min read ·
Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Shaking up the landslide model

1 min read ·
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

Testing barley's salt tolerance is a numbers game

1 min read ·
Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.

A sharper view of flood risk

1 min read ·
Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Blueprint may power up KSA's wind energy future

1 min read ·
High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

Wind forecasts power up for reliable energy production

1 min read ·
Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Modeling the neighborhood boosts landslide prediction

1 min read ·
A prediction model that considers multiple landslides over time in a given region may improve the accuracy of early warning systems.

KAUST STAT students come together for second ASA chapter meeting

1 min read ·
Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Modeling rainfall drop by drop

1 min read ·
A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Getting the inside track on street design

1 min read ·
Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

A brainwave from social networks

1 min read ·
The statistics used to understand social networks reveal the diversity of functional connections in the brain.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Heatwave model shows it is hotter in more places, more often

1 min read ·
New modeling shows that heatwaves across Europe have increased in both frequency and spatial extent over the past century.

Deeper defense against cyber attacks

1 min read ·
To address the growing threat of cyberattacks on industrial control systems, a KAUST team including Fouzi Harrou, Wu Wang and led by Ying Sun has developed an improved method for detecting malicious intrusions.

Finding true protein hotspots in cancer research

1 min read ·
Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Professor Bolin earns American Statistical Association Section on Statistics and the Environment Early Investigator Award

1 min read ·
Prof. David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.
<> applied mathematics (48)

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

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B9, L2, R2322

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

Towards Flexible Polyhedral Nets

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B4 L5 R5209

This thesis research provides a comprehensive classification of flexible geometric nets of arbitrary size in both Euclidean and isotropic geometries, revealing that only two distinct classes exist in each setting and demonstrating their relationship to mechanical design.

Discovery of Low-Dimensional Generative Models for Complex Dynamical Systems

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This thesis presents a data-driven framework for discovering low-dimensional generative models of complex systems by using a library of normal-form equations to identify both observable dynamics and hidden control variables directly from time-series data.

Juan Pablo Muñoz Díaz

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.
Rihab Larayedh

Rihab Larayedh

Dedicated researcher in underwater acoustics , specifically investigating shipping noise from vessels, to enhance our understanding of its environmental impact and develop strategies for noise mitigation

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Numerical Approximation of the Stochastic Cahn-Hilliard Equation and the (Stochastic) Hele-Shaw Problem

B1 L2 R2202

The Cahn-Hilliard equation is a fourth order parabolic partial differential equation (PDE) that is widely used as a phenomenological model to describe the evolution of interfaces in many practical problems, such as, the microstructure formation in materials, fluid flow, etc. It has been observed in the engineering literature that the stochastic version of the Cahn-Hilliard equation provides a better description of the experimentally observed evolution of complex microstructure.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

The role of applied mathematics in finance

1 min read ·
“Sometimes there’s a strange dichotomy between applied mathematics and pure mathematics,” said Professor Raul Tempone, a founding KAUST faculty member and principal investigator of the University's Stochastic Numerics Research Group. “People think that applied math is simply the application of theory.” The argument is that applied math is just classified by its ends in the sense that one is trying to solve a real problem. Tempone argues that this doesn’t mean that new theories don’t need to be created. It also does not mean that the kinds of problems are less challenging than the ones faced in

Bringing computational engineering into the fold

1 min read ·
An interactive computational tool opens new possibilities for "developable" surfaces.

Aesthetic patterns for freeform architecture

1 min read ·
Planar designs are used to model complex curved surfaces, with applications from architecture to 3D prints.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

A clearer view of rainfall patterns

1 min read ·
Improved random modeling allows scientists to generate realistic patterns of high-frequency rainfall.

Getting a handle on extremes

1 min read ·
By tapping into the power of extreme value theory, an international team of researchers including Raphaël Huser from the University's Computer, Electrical and Mathematical Science and Engineering Division has developed a statistical model that overcomes the shortcomings of previous schemes to provide a reliable basis for climate research and the prediction of drought and flood. The model can accurately describe observed rainfall data and reliably predict the likelihood of future extreme events. The analysis of hourly or daily rainfall data presents many challenges for researchers and

Making space for climate simulations

1 min read ·
A statistics-based data compression scheme cuts data storage requirements for large-scale climate simulations by as much as 98 percent.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Tunable sound transmission shapes up

1 min read ·
Carefully designed crystal structures can now be tuned to control how they transmit acoustic waves.

Color-changing coatings ready for the big time

1 min read ·
The dazzling colors of peacock feathers arise from the physical interaction of light with biological nanostructures. Researchers have discovered how to exploit this natural trickery known as structural coloration into a large-scale printing technology that produces lightweight and ultraresistant coatings in any color desirable.

Getting the jump on industrial failures

1 min read ·
A statistics-driven method for the early detection of emerging problems in industrial processes could improve industrial safety, reliability and productivity.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Keeping antennas at peak performance

1 min read ·
Sensitive detection of partial faults in antenna systems could prevent performance degradation in wireless networks.

Taking graphics cards beyond gaming

1 min read ·
A highly efficient mathematical solver designed to run on graphics processors gives scientists and engineers a powerful new tool for a common computational problem.

A safe sweep of weather data

1 min read ·
A statistical technique for automatically cleaning erroneous data from weather-balloon observations will improve the accuracy of weather forecasting.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

Signaling success for 5th gen communications

1 min read ·
An efficient wireless signal optimization scheme achieves peak performance for next-generation mobile communications.

Getting a visual on complex data

1 min read ·
A new method for visually presenting complex data distributions provides a much-needed tool for management, analysis and interpretation.

Tiny speakers break a barrier for sound

1 min read ·
A loudspeaker design could allow small devices to produce powerful low-frequency sounds.

Making loudspeakers resonate like atoms

1 min read ·
Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

Better racing car design through an industry partnership

1 min read ·
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

A clique away from more efficient networks

1 min read ·
An old branch of mathematics finds a fertile new field of application.

Unleashing the potential of tethered drones

1 min read ·
Wire-connected drones may complement or replace the fixed base stations of cellular communications networks.

Quieter wind beneath the wings

1 min read ·
The ability to efficiently simulate the noise generated by wings and propellers promises to accelerate the development of quieter aircraft and turbines.

Mathematician Peter Markowich named 2022 AMS Fellow

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich joins 45 mathematical scientists from around the world named 2022 Fellows of the American Mathematical Society (AMS) program.

Professor Athanasios Tzavaras elected Fellow of the European Academy of Sciences (EurASc)

1 min read ·
Athanasios (Thanos) Tzavaras has been elected Fellow of the European Academy of Sciences (EurASc). He was nominated for his contributions to the interface of nonlinear partial differential equations and applied mathematics in the physical sciences.

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Pirahmad Olimjoni

Mohammed Sayyari

Mohammed is a Mathematician with a solid background in scientific programming. Mohammed's mathematical interests are in the analysis of partial differential equations and the discretization thereof.

Linda Alzaben

Linda Alzaben is a Ph.D. candidate in Applied Mathematics and Computational Science. Linda has a Bachelor's and a Master's degree, both in pure Mathematics, from King Abdulaziz University. In both her B.Sc. and M.Sc. she was first-class honors. Although she had been teaching for several years she decided to pursue her postgraduate studies in an engaging and interesting field at KAUST. She seized the opportunity to study and work under the supervision of Professor Daniele Boffi who is a distinguished professor in his field. Her main interest are in Finite elements and Mixed Finite elements and

Jinchao Xu

Professor Xu is widely recognized as a leading figure in developing, designing and analyzing fast methods for finite element discretization and large-scale equation solutions.

Georgiy L. Stenchikov

Emeritus Professor (former), Earth Science and Engineering
Georgiy L. Stenchikov, a former KAUST faculty member, is renowned for his work in climate modeling, atmospheric processes, and volcanic impacts.

Aimad ER-RAIY

Aimad Er-Raiy joined the Extreme Computing Research Center (ECRC) at King Abdullah University of Science & Technology (KAUST) as a postdoctoral fellow in late 2019. He is working with Professor Matteo Parsani. Prior to this, he spent a year as a Postdoctoral Research Fellow in CERFACS (Toulouse, France). Education and Early Career Dr. Er-Raiy received a MSc. (2014) from the National Institute of Posts and Telecommunications (INPT) in Toulouse, France, and a Ph.D. (2018) from Institut Pprime & National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA) in Poitiers (France). Research
<> optimization (37)

Approximation and Optimization for Neural Networks

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In this talk, we consider new connections between the approximation and optimization of neural networks. Instead of relying on excessive over-parametrization to achieve zero training loss, we identify good minima by comparison with established approximation bounds.

A Dichotomy of Continuous Finite Element Spaces and Its Application to Energy-Conservative Galerkin Methods for Nonlinear and Dispersive Wave Equations

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B1 L3 R3119

This talk illustrates why energy-conservative Galerkin methods for nonlinear and dispersive wave equations achieve optimal convergence rate with odd-degree polynomials.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Mathematical Imaging in the Era of AI

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B1 L23 R3119

In this talk, I will present mathematical imaging as a meeting point of classical analysis and modern AI. I will highlight how ideas from optimisation and mathematical modelling can guide the development of structure-preserving deep learning methods, offering new, principled approaches to large-scale inverse imaging problems.

First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

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B9 L2 R2325

This talk will discuss how to design asynchronous optimization methods that remain fast, stable, and even provably optimal.

Towards Flexible Polyhedral Nets

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B4 L5 R5209

This thesis research provides a comprehensive classification of flexible geometric nets of arbitrary size in both Euclidean and isotropic geometries, revealing that only two distinct classes exist in each setting and demonstrating their relationship to mechanical design.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Randomized Greedy Algorithms for Neural Network Optimization in Solving Partial Differential Equations

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B4 L5 R5220

This thesis introduces the randomized orthogonal greedy algorithm (ROGA) to bridge the gap between theoretical and practical performance of shallow neural networks for solving partial differential equations by overcoming key optimization challenges to achieve provably optimal convergence rates.
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

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B1 L3 R3119

This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.

Convergence of Descent Methods under Polyak- Lojasiewicz-Kurdyka Properties

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B1 L3 R3119

This talk presents a novel convergence analysis for a class of descent methods in nonsmooth and nonconvex optimization under the Polyak-Lojasiewicz-Kurdyka properties, proving finite termination under certain conditions and revealing incompatibilities between these properties and gradient Lipschitz continuity in certain frameworks.

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Estimating the expected information gain in Bayesian optimal experimental design

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B9 L2 H1

Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Dynamics and Convergence of Weight Normalization for Training Neural Networks

Guido Montufar, Assistant Professor, Departments of Mathematics and Statistics, University of California, Los Angeles (UCLA)

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B1 L3 R3119

We present a result on the convergence of weight normalized training of artificial neural networks. In the analysis, we consider over-parameterized 2-layer networks with rectified linear units (ReLUs) initialized at random and trained with batch gradient descent and a fixed step size. The proof builds on recent theoretical works that bound the trajectory of parameters from their initialization and monitor the network predictions via the evolution of a ''neural tangent kernel'' (Jacot et al. 2018). We discover that training with weight normalization decomposes such a kernel via the so called ''length-direction decoupling''. This in turn leads to two convergence regimes. From the modified convergence we make a few curious observations including a natural form of ''lazy training'' where the direction of each weight vector remains stationary.

Quantitative understanding of transcription factor regulation at network- and molecular-levels through optimization and deep learning

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KAUST

Transcription factors are an important family of proteins that control the transcription rate from DNAs to messenger RNAs through the binding to specific DNA sequences. Transcription factor regulation is thus fundamental to understanding not only the system-level behaviors of gene regulatory networks, but also the molecular mechanisms underpinning endogenous gene regulation. In this talk, I will introduce our efforts on developing novel optimization and deep learning methods to quantitatively understanding transcription factor regulation at network- and molecular-levels. Specifically, I will talk about how we estimate the kinetic parameters from sparse time-series readout of gene circuit models, and how we model the relationship between the transcription factor binding sites and their binding affinities.

Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

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KAUST

In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. The latter are used to specify both the ocean current and the bathymetry. We apply a NLP to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. Advanced visualization tools are used to amplify simulation results.

Powering the bottom line

1 min read ·
A risk-based optimization scheme boosts confidence and profitability for future mixed-technology power plants.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Meet KAUST Prospective Student: Igor Sokolov

1 min read ·
Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

Meet KAUST student: Grigory Malinovsky

1 min read ·
Grigory Malinovsky joined KAUST this fall as a M.S./Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab. Grigory holds a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Malinovsky first came to KAUST as a visiting student and was instantly impressed by the research facilities and working atmosphere. His outstanding internship experience was a catalyst in choosing KAUST as the next step in his academic career.

Yongliang Yang

Research Scientist, Computer Science
Yongliang Yang was a Research Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in Department of Computer Science at University of Bath in United Kingdom. Education and Early Career Yongliang received his Bachelor degree in Computer Science and Technology in 2004, from Tsinghua University. He continued to pursue his Master and Ph.D. degree in Graphics and Geometric Computing Group in Tsinghua, under the supervision of Prof. Shi-Min Hu. In 2009, he joined KAUST as a Postdoctoral Fellow working with Prof

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Rafal Szlendak

Rafał Szlendak is an MS/PhD student in the AMCS program under the supervision of Prof. Peter Richtarik. His biggest passions are research, weightlifting and travelling. His career path is motivated by his endless passion for self-development and discovery. Education and Early Career Rafał Szlendak has obtained his Bachelor's degree in Mathematics and Statistics from University of Warwick Research Interest In his research Rafał focuses on stochastic gradient descent algorithms for large-scale machine learning problems. His primary goal is developing state-of-the-art methods with sound

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Mher Safaryan

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Mher Safaryan is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Mher received his Ph.D. in Mathematics from Yerevan State University, Armenia under the supervision of Prof. Grigori Karagulyan. Before joining KAUST, he was a Junior Researcher at the Institute of Mathematics of National Academy of Sciences, Real Analysis Department in Armenia. After that, he joined the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE)

Laurent Condat

Senior Research Scientist, Computer Science
I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Igor Sokolov

Igor Sokolov is an M.S. candidate in the KAUST Peter Richtarik Research Group under the supervision of Professor Peter Richtarik. Before joining KAUST, Igor obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Igor's research interests lie in the intersection of optimization and machine learning. He is interested in both the theoretical analysis of optimization algorithms as well as their practically efficient applications.

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied

Ganesh Sundaramoorthi

Ganesh Sundaramoorthi was an Assistant Professor of Electrical Engineering and jointly Assistant Professor of Applied Mathematics and Computational Science at KAUST starting in 2011. He is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, formulating computer vision algorithms for robotic inspection applications. Education and Early Career His PhD is in Electrical and Computer Engineering from the Georgia Institute of Technology in Atlanta, GA, USA in 2008. His PhD developed fundamental shape optimization methods for computer vision that

Filip Hanzely

Filip Hanzely is a recent PhD graduate from the group of Peter Richtarik. His research focuses mostly on various aspects of optimization for machine learning, designing provably efficient algorithms for solving big data problems. Filip is joining Toyota Technological Institute in Chicago (TTIC) as a research assistant professor in December 2020. Education and Early Career Filip Hanzely graduated with honor for his bachelor degree in Economics and Financial Mathematics from Comenius University in Bratislava, Slovakia. Later on in 2017, he received his master in science degree in Mathematics and

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria

Alexandra Aguiar Gomes

Alexandra Aguiar Gomes has been a university educator since 1996. Her area of expertise is optimization, with current interests in the mathematics of decision-making for sustainable development and the mathematics of generative artificial intelligence. Alexandra finds joy in simplifying mathematical concepts, helping her students discover the practicality of mathematics in their fields.
<> numerical analysis (35)
Banner SNSL 2026 Workshop

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

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Auditorium 0215 between B4 & B5

Workshops
Bringing together experts in stochastic algorithms, numerical analysis and statistical learning with applications in climate modeling and quantitative finance.

Lajos Lóczi

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

Abdul-Lateef Haji-Ali

Shyam Mohan Subbiah Pillai

Abderrahmene Ben Romdhane

Arved Bartuska

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Theory and Implementation of Novel Numerical Methods for Multiphysics Interface Problems

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B2/B3 L0 R0215

This thesis develops and analyzes Finite Element Methods (FEM) for multiphysics interface problems using the Fictitious Domain with Distributed Lagrange Multiplier (FD-DLM) framework. It introduces new families of stable mixed methods with discontinuous Lagrange multiplier spaces, studies both a priori and a posteriori error estimates, and designs multigrid preconditioners. Theoretical results are supported by numerical experiments.
KAUST-CEMSE-NumPDE-Workshop-2024

NumPDE Workshop: Numerical Analysis of PDEs

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Sunday 26/01 (morning, 8.45-13.30) - Auditorium between Building 2 and Building 3; Sunday 26/01 (afternoon, from 13.45) - Building 9, Room 2322 (Lecture Hall); Monday 27/01 and Tuesday 28/01 - Auditorium between Building 4 and Building 5

Explore cutting-edge numerical analysis of PDEs, including Finite Element Methods, Virtual Element Methods, and Isogeometric Analysis, with leading experts.
Georgios Grekas

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Georgios Grekas research focuses on mathematical modeling (biomechanics, solid mechanics), numerical analysis, scientific and parallel computing, and scientific machine learning.

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Numerical methods for stochastic conservation laws with multiplicative rough drivers in the flux

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B1 L4 R4214

Abstract Stochastic conservation laws (SCL) with quasilinear multiplicative``rough'' path dependence in the flux arise in modeling of mean field games. An impressive collection of theoretical results has been developed for SCL in recent years by Gess, Lions, Perthame, and Souganidis. We present the first fully computable numerical methods for pathwise solutions of scalar SCL with, for instance, "rough" paths in the form of Wiener processes. Convergence rates are derived for the numerical methods and we show that for strictly convex flux functions, "rough" path oscillations lead to

An introduction to Patankar-type schemes

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B9 L3 R3229

We present order conditions for various Patankar-type schemes as well as a new stability approach that examines the non-hyperbolic fixed points of the schemes for a general linear test problem. We formulate sufficient conditions for the stability of such non-hyperbolic fixed points and the local convergence of the numerical approximation towards the correct steady-state solution of the underlying test problem. To illustrate the theoretical results, we consider several members of the modified Patankar-type family within numerical experiments.

Hierarchical ensemble-based data assimilation and rare event tracking

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B4 L5 R5220

This thesis consists of three papers considering, in general, two challenges: the minimization of computational cost in the ensemble Kalman filtering (EnKF) method and the problem of tracking a rare event within the framework of the EnKF.

KAUST Associate Professor Taous-Meriem Laleg-Kirati finalist at Leadership Excellence for Women Awards & Symposium

1 min read ·
Taous-Meriem Laleg-Kirati, KAUST associate professor of electrical engineering in the University's Computer, Electrical and Mathematical Science and Engineering Division, was selected as one of three finalists in the academic of distinction category at the recent Leadership Excellence for Women Awards & Symposium (LEWAS) in Bahrain.

CEMSE New Faculty: Daniele Boffi, Professor of Numerical Analysis

1 min read ·
Daniele Boffi is a professor of numerical analysis who recently joined the KAUST Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division.

Understanding the COVID wave

1 min read ·
As countries around the world work to "flatten the curve," a reference to slowing the progression of COVID-19 cases, David Ketcheson is putting mathematical modeling to work to understand how transmission is playing out.

A shock to the system

1 min read ·
The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

KAUST mathematician named Fellow to European Academy of Sciences

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science, Dr. Peter Markowich, is named a 2020 Fellow to the European Academy of Sciences (EurASc) in recognition of his outstanding scholarship in the field of mathematical and numerical analysis of partial differential equations.

Mathematician Peter Markowich named 2022 AMS Fellow

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich joins 45 mathematical scientists from around the world named 2022 Fellows of the American Mathematical Society (AMS) program.

Yang Liu

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Education Profile B.Eng. Aerospace Engineering, Beihang University, Beijing, China, 2017. M.Sc. Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2019. Ph.D. Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2024. Research Interests Yang's research interests include ​Probability Theory and Stochastic Processes, Numerical Analysis.

Waleed Alhaddad

Waleed Alhaddad obtained his Master's degree in Applied Mathematics and Computational Sciences from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Raul F. Tempone at Stochastic Numerics Research Group (STOCHNUM). Research Interests Waleed's research interests include ​Mathematical Modeling, Probability Theory and Stochastic Processes, Numerical Analysis. Education Profile ​M.Sc. Applied Mathematics, Finance, King Abdullah University of Science and Technology (KAUST) 2016-2018. B.Sc. Physics, University of California Santa Barbara 2016. Awards and

Umberto Zerbinati

Sören Wolfers

Sören Wolfers obtained a Ph.D. degree in Applied Mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sören's research interests included Numerical Analysis, Uncertainty Quantification, Sparse Approximation, Multilevel methods and Meshfree methods. Selected Publications Beck, J., Roberts, G., and Wolfers, S. (2018). Bayesian earthquake dating and seismic hazard assessment using chlorine-36 measurements (BED v1). Submitted to

Raul Tempone

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Najwa Alshehri

Professional Interests Beyond academic and research pursuits, Najwa is deeply committed to mentoring and empowering female students to pursue careers in science and research. At KAUST, she engaged in initiatives that cultivate talent in mathematics, AI, and deep learning. Additionally, she placed significant emphasis on developing her leadership and entrepreneurial skills through specialized programs and certifications along with other trainings in Key Performance Indicators (KPI), Total Quality Management (TQM), and strategic planning.

Linda Alzaben

Linda Alzaben is a Ph.D. candidate in Applied Mathematics and Computational Science. Linda has a Bachelor's and a Master's degree, both in pure Mathematics, from King Abdulaziz University. In both her B.Sc. and M.Sc. she was first-class honors. Although she had been teaching for several years she decided to pursue her postgraduate studies in an engaging and interesting field at KAUST. She seized the opportunity to study and work under the supervision of Professor Daniele Boffi who is a distinguished professor in his field. Her main interest are in Finite elements and Mixed Finite elements and

Juncai He

I am currently a research scientist in Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) at King Abdullah University of Science and Technology (KAUST). I received a B.S. degree in Mathematics and Applied Mathematics from Sichuan University in 2014. In the Summer of 2019, I received my Ph.D. degree in Computational Mathematics under the supervision of Prof. Jinchao Xu and Prof. Jun Hu at Peking University in Beijing, China. From 2019 to 2020, I worked as a Postdoctoral Scholar supervised by Prof. Jinchao Xu in The Center for Computational Mathematics and Application

Jongho Park

Jongho Park is a Research Scientist at King Abdullah University of Science and Technology (KAUST) where his research focuses on the design and analysis of efficient numerical methods for variational problems.

Håkon A. Hoel

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations

Gaukhar Shaimerdenova

Gaukhar Shaimerdenova is a consultant at the Stochastic Numerics Research Group (STOCHNUM) of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Gaukhar's research interests include ​Stochastic Differential Equations, Numerical Analysis, Data Assimilation, Multilevel/Multi-index Monte Carlo methods, Ensemble Kalman Filtering and Computational Finance. Selected Publications Rached, N.B., Schwerin, E.V., Shaimerdenova, G. and Tempone, R., 2026. Importance sampling for rare event tracking within the ensemble Kalman filtering framework

Evangelia Kalligiannaki

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

Erik von Schwerin

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.

Carlos Andrés Muñoz Moncayo

Carlos Andrés Muñoz Moncayo is an M.S./Ph.d. candidate in applied mathematics and computational sciences program and a member of The Numerical Mathematics Group under the supervision of Professor David Ketcheson at King Abdullah University of Science and Technology (KAUST). Research Interests Carlos' research interests include partial differential equations, numerical analysis, and scientific computing.
<> numerical methods (29)

Extrapolated Linear Multistep Methods

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B9 L2 R2325

In this talk, we will discuss how linear multistep methods and classical extrapolation can be combined to obtain new classes of efficient time-integration methods.

Lajos Lóczi

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

Salim Ksous

Realistic scenario planning for solar power

1 min read ·
Placing realistic bounds on solar power scenarios enables smarter grid planning and energy management.

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

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B9, L2, R2322

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

KAUST master’s student Osama Dabbousi awarded Rhodes Scholarship

1 min read ·
KAUST master’s student Osama Dabbousi earns the prestigious Rhodes Scholarship, reflecting KAUST’s growing role in developing national research talent.

Mathematical Modeling of Two-Population Pedestrian Congestion

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B1 L3 R3426

This seminar introduces a novel class of cross-diffusion systems for pedestrian dynamics governed by internal energy minimization under congestion.

Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

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B1 L3 R3119

This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Mini Course Part 2-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

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B1 L3 R3119

In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation

Mini Course Part 3-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

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B1 L3 R3119

In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation

Mini Course Part 1-3 - Modelling and numerical methods for diffuse interface models in compressible multi-phase flows

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B1 L3 R3119

In this course we consider multi-phase flows, i.e., flows of one substance which is present as liquid as well as vapor. We focus on models that resolve individual bubbles/droplets and that treat both phases as compressible. We will also discuss incompressible/low Mach limits, since in most applications the liquid is nearly incompressible. Understanding and simulating such small-scale models is important in order to obtain information which can be used in larger scale models for e.g. sprays which play important roles in processes of practical interest as diverse as combustion, chemical engineering, and cloud formation

Numerical methods for stochastic conservation laws with multiplicative rough drivers in the flux

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B1 L4 R4214

Abstract Stochastic conservation laws (SCL) with quasilinear multiplicative``rough'' path dependence in the flux arise in modeling of mean field games. An impressive collection of theoretical results has been developed for SCL in recent years by Gess, Lions, Perthame, and Souganidis. We present the first fully computable numerical methods for pathwise solutions of scalar SCL with, for instance, "rough" paths in the form of Wiener processes. Convergence rates are derived for the numerical methods and we show that for strictly convex flux functions, "rough" path oscillations lead to

Energy and Entropy in Numerical Methods

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B9 L2 H2

Secondary quantities such as energy and entropy can be very important for numerical methods. Firstly, preserving these quantities can ensure that non-physical behavior is excluded. Secondly, preserving such quantities can result in stability estimates. Finally, preserving the correct energy/entropy evolution in time can result in additional desirable properties such as lower numerical errors. In this talk, a brief overview of some recent advances concerning energy and entropy preserving numerical methods for ordinary and partial differential equations will be given, together with an outlook on future research directions and applications.

Deterministic particle approximation for nonlinear transport equations

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Building L3 R3119

Approximating the solution to an evolutionary partial differential equation by a set of "moving particles" has several advantages. It validates the use of a continuity equation in an "individuals-based" modeling setting, it provides a link between Lagrangian and Eulerian description, and it defines a "natural" numerical approach to those equations. I will describe recent rigorous results in that context. The main one deals with one-dimensional scalar conservation laws with nonnegative initial data, for which we prove that the a suitably designed "follow-the-leader" particle scheme approximates entropy solutions in the sense of Kruzkov in the many particle limit. Said result represents a new way to solve scalar conservation laws with bounded and integrable initial data. The same method applies to second order traffic flow models, to nonlocal transport equations, and to the Hughes model for pedestrian movements.

Mean Field Games: Numerical Methods and Applications

Mathieu Laurière, Postdoc, Department of Operations Research and Financial Engineering, Princeton University

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KAUST

In this talk, several numerical methods will be presented and illustrated on examples. Borrowing tools from stochastic analysis, optimization, partial differential equations and machine learning, these methods enable us to solve mean field games with possibly complex sources of noise or high dimensional state variables.

International Seminar on Mathematical Models and Numerical Methods for Flow and Transport in Porous Media

Speakers from KAUST, CEMSE, PSE, G-CSC, IBRAE and INM RAS

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KAUST

This workshop is devoted to numerical simulation of groundwater flow and subsurface contamination transport, as well as related problems. The main topics are mathematical modeling of the processes in porous media and the numerical methods for discretization, solution of the discretized systems and numerical treatment of inverse problems. In particular, fractured porous media and partially saturated aquifers will be concerned.

Asymptotic Analysis and Numerical Methods for Compressible Flows at Low Mach Numbers

Prof. Dr. Andreas Meister, Professor, Department of Mathematics and Natural Sciences, University of Kassel

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B1 L4 R4102

We will present a comprehensive study of a finite volume method for inviscid and viscous flow fields at high and low speeds. Thereby, the results of a formal asymptotic low Mach number analysis are used to extend the validity of the numerical method from the simulation of compressible flow fields at transonic as well as supersonic speed to the low Mach number regime.

Numerical Methods for Energy Minimisation: Application to Machine Learning Algorithms

Prof. Charalambos Makridakis, Director of the Institute of Applied and Computational Mathematics of FORTH, Greece

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B2 L5 R5220

In this talk, we discuss problems and numerical methods arising in the calculus of variations and energy minimization. Among numerous applications, energy minimization is a core element of Machine Learning algorithms. Within the field of nonlinear PDEs, the calculus of variations has received a lot of attention from the analysis point of view. Although quite interesting and challenging, the numerical analysis of these problems is much less developed.

Multiscale numerical methods. Theory and Practice

Yalchin Efendiev, Professor, Institute for Scientific Computation, Texas A and M University

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B9 L3 R3128

In this talk, I will talk about general approaches for multiscale modeling (closely related to porous media applications). I will mainly focus on numerical approaches, where multiscale finite element basis functions are constructed and used in approximating the solution. In these approaches, macroscopic equations are formed via some variational formulations of the problem. I will discuss how these approaches are used in deriving, so called upscaling techniques and the relation to well known upscaling methods. The concepts discussed in the talk are used for linear and nonlinear problems. I will discuss some applications.

Fast Solution Methods for Wave Propagation Problems: From Classical Domain Decomposition Solvers to Learning

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B2 L5 R5220

Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

1 min read ·
The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Understanding the COVID wave

1 min read ·
As countries around the world work to "flatten the curve," a reference to slowing the progression of COVID-19 cases, David Ketcheson is putting mathematical modeling to work to understand how transmission is playing out.

Gabriel Wittum

Dr. Gabriel Wittum, professor of applied mathematics and computational science at KAUST, is an expert in modelling and simulating problems in empirical science.

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group
André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, ​the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications​ A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in

Aku-Jaakko Alexis Kammonen

Aku-Jaakko Alexis Kammonen is a Research Scientist at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST).
<> Deep learning (25)

Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective

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B1 L3 R3119

This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Discovery of Low-Dimensional Generative Models for Complex Dynamical Systems

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This thesis presents a data-driven framework for discovering low-dimensional generative models of complex systems by using a library of normal-form equations to identify both observable dynamics and hidden control variables directly from time-series data.

Juan Pablo Muñoz Díaz

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Mhd. Emad Alolaby

Sixiu Liu

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Jinchao Xu

Professor Xu is widely recognized as a leading figure in developing, designing and analyzing fast methods for finite element discretization and large-scale equation solutions.
<> artificial intelligence (24)

Yanda Meng

Assistant Professor, Bioengineering
Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

KAUST becomes first FIFA research institute in the Middle East and Asia

1 min read ·
KAUST has been designated the first FIFA Research Institute in the Middle East and Asia, and the fifth worldwide, reflecting the University’s commitment to advancing innovation in football through cutting-edge scientific inquiry.

KAUST, in collaboration with MCIT, to host the second edition of the MENA Machine Learning Winter School (MenaML)

1 min read ·
A week of advanced instruction and exchange for emerging machine learning talent.

Data-driven Anomaly Detection in Industrial Processes

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B9 L2 R2325

This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Nuclear Fusion Powered by AI and HPC

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B9 L2 R2325

This talk presents advanced simulations with AI-driven optimization to improve the performance of a next-generation plasma-jet-driven magneto-inertial fusion concept.
Salem Alsaqaf

Salem Al-saqaf

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.
Najmeddine Dhieb

Najmeddine Dhieb

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

KAUST Research Conference Digital Health 2020

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B19 L2 H1

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Public Colloquium

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B9 L2 R2322

Applied Complexity aims to understand the physical origin of these behaviors and transform them into sustainable technologies that tackle global problems of global interest. These range from energy harvesting to clean water production, the design of smart materials, biomedical applications, information security, artificial intelligence, and global warming. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.

Adaptive Optimization Algorithms for Machine Learning

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B1 L4 R4214

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

Alexandra Aguiar Gomes

Alexandra Aguiar Gomes has been a university educator since 1996. Her area of expertise is optimization, with current interests in the mathematics of decision-making for sustainable development and the mathematics of generative artificial intelligence. Alexandra finds joy in simplifying mathematical concepts, helping her students discover the practicality of mathematics in their fields.
<> Partial Differential Equations (24)

Katerina Nik

Professor Nik studies partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering.

Part 3: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems.

Part 1: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 1: I will show how to transform suitable mean-field game (MFG) systems into infinite-dimensional convex optimization problems. Furthermore, I will present Uzawa’s algorithm and augmented Lagrangian approach for solving convex optimization problems. Finally, I will demonstrate how to apply these methods to approximate solutions of corresponding MFG systems.

Estimation Problems for Partial and Fractional Differential Equations - Spring 2019

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B9 L2 H1

We will present some new methods for source and parameters estimation for partial and fractional differential equations and illustrate the results with some simulations and real applications.

Part 2: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 2: I will present new Fourier approximation techniques for nonlocal MFG systems and discuss how these techniques fit the variational framework presented on Day 1. Then I will introduce the Chambolle-Pock primal-dual hybrid gradient optimization method and apply a variant of this method to approximate nonlocal MFG models via Fourier approximation techniques above.

Part 3: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 3: I will present Fuglede’s proof of the quantitative isoperimetric inequality for convex and for nearly spherical sets

Part 1: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 1: I will introduce some basic notions of the theory of sets of ignite perimeter and of geometric measure theory.

Part 1: Boundary Control of Continuum Problems

Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

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B9 L3 R3131

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Part 2: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 2: I will present De Giorgi’s proof of the isoperimetric inequality and comment on other proofs of the isoperimetric inequality

Part 4: The isoperimetric inequality. Stability and related topics

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B1 L4 R4102

The aim of the course is to give a self contained introduction, at the level of a graduate course, to the stability of the isoperimetric inequality and other related geometric and functional inequality such as the Sobolev inequality, the Faber-Krahn and the Brunn-Minkowski inequality. Lecture 4: I will discuss the quantitative isoperimetric inequality in the general case and the quantitative form of related geometric and functional inequalities.

Part 2: Boundary Control of Continuum Problems

Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

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B9 L3 R3131

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Part 3: Boundary Control of Continuum Problems

Professor Mamadou L. Diagne, Rensselaer Polytechnic Institute

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B9 L3 R3131

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite-dimensional representation. This course will explore the boundary control of several class of PDEs via the well-known backstepping method.

Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

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B9 L2 H1 R2322

I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.

On the collapse mechanism of Wave Maps

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B1 L4 R4214

The Wave Map system describes the evolution of waves constrained on a (Riemannian) manifold. For the 2 + 1 dimensional problem, when the target manifold is a sphere, the solution collapses in finite time. The Analysis is due to the pioneering work of Merle, Paphael and Rodnianski. Motivated by their work I will present a somewhat novel approach of the collapsing mechanism which is based on a view of the equations as a nonlinear gauge system. This is joint work with Dan Geba.

Aggregation-Diffusion and Kinetic PDEs for collective behavior: applications in the sciences

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B1 L3 R3119

In this seminar, I will present a survey of micro, meso and macroscopic models where repulsion and attraction effects are included through pairwise potentials. I will discuss their interesting mathematical features and applications in mathematical biology and engineering. Qualitative properties of local minimizers of the interaction energies are crucial in order to understand these complex behaviors. I will showcase the breadth of possible applications with three different phenomena in applications: segregation, phase transitions, and consensus.

Tensor-train Methods for Partial Differential Equations and its application to a Neutron Transport Problem

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B9 L4 R4225

Tensor network techniques are known for their ability to approximate low-rank structures and beat the curse of dimensionality. They are also increasingly acknowledged as fundamental mathematical tools for efficiently solving high-dimensional Partial Differential Equations (PDEs). In this talk, we present a novel method that incorporates the Tensor Train (TT) and Quantized Tensor Train (QTT) formats for the computational resolution of time-independent Boltzmann Neutron Transport equations (BNTEs) in Cartesian coordinates.

KAUST mathematician named Fellow to European Academy of Sciences

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science, Dr. Peter Markowich, is named a 2020 Fellow to the European Academy of Sciences (EurASc) in recognition of his outstanding scholarship in the field of mathematical and numerical analysis of partial differential equations.

Meet KAUST prospective student: Stefanos Georgiadis

1 min read ·
Stefanos Georgiadis is an applied mathematics graduate who will join KAUST from the School of Applied Mathematical and Physical Sciences, Athens, Greece. Stefanos will join KAUST this fall as a Ph.D. candidate and member of the Applied Partial Differential Equations (AppliedPDE) research group under the supervision of Professor Athanasios Tzavaras.

Stefanos Georgiadis

Stefanos Georgiadis is a Ph.D. candidate in a co-tutelle between KAUST and TU Wien, under the supervision of Professor Athanasios Tzavaras and Ansgar Jüngel. Before joining KAUST, Stefanos obtained his bachelor and master's degree in applied mathematics from the School of Applied Mathematical and Physical Sciences of the National Technical University of Athens in Greece. Research Interests Stefanos' research interests include the analysis of nonlinear partial differential equations as they apply to the modeling of multicomponent systems of gases.

Nuno J. Alves

Short Bio Mr. Nuno Alves is a Ph.D student in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. He has got bachelor and master's degrees from Instituto Superior Tecnico, University of Lisbon, Portugal. Mr. Nuno had been a visiting student at KAUST from March 2014 to July 2014. Moreover, during the academic year 2017/2018 he was working as an invited teaching assistant at Polytechnic Institute of Beja, Portugal. His work is essentially related with non-linear partial

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Carlos Andrés Muñoz Moncayo

Carlos Andrés Muñoz Moncayo is an M.S./Ph.d. candidate in applied mathematics and computational sciences program and a member of The Numerical Mathematics Group under the supervision of Professor David Ketcheson at King Abdullah University of Science and Technology (KAUST). Research Interests Carlos' research interests include partial differential equations, numerical analysis, and scientific computing.

Athanasios Tzavaras

KAUST Professor of Applied Mathematics and Computational Science Athanasios (Thanos) Tzavaras specializes in mathematical modelling, analysis and computation of partial differential equations pertaining to fluid mechanics or modeling of materials.
<> uncertainty quantification (24)

Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective

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B1 L3 R3119

This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.

Abdul-Lateef Haji-Ali

Shyam Mohan Subbiah Pillai

Nabila Bounceur

My work focuses on the modeling of complex systems from the Probabilistic Sensitivity Analysis (PSA) and Uncertainty Quantification (UQ) perspective. I develop methodologies to efficiently characterize input-output relationships in computationally expensive simulators, enabling both qualitative understanding (which factors matter most?) and quantitative assessment (how much does each factor contribute to output uncertainty?).

Abderrahmene Ben Romdhane

Arved Bartuska

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Amal Alghamdi

KAUST alumnus Dr. Abdul-Lateef Haji-Ali receives prestigious Humboldt Research Fellowship for Experienced Researchers

2 min read ·
KAUST alumnus Abdul-Lateef Haji-Ali receives Humboldt Fellowship for advances in stochastic modeling
ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Cross-disciplinary collaboration results in groundbreaking earthquake research

1 min read ·
For KAUST research assistant Dr. Ling Zhang and Ph.D. student Hoby Razafindrakoto, a class in statistics the two took together on campus was a serendipitous event. "Hoby and Ling came to my spatial statistics class in the spring of 2013," explained Marc Genton, Professor of Statistics in the Computer, Electrical and Mathematical Science and Engineering Division. "They had to do a project for the class, and their project morphed into a collaboration that produced two papers and an online tool for uncertainty quantification in earthquake source modeling." The group effort included P. Martin Mai

Study finds Red Sea may be cooling rather than warming

1 min read ·
A recent study by a team including KAUST Earth scientists and oceanographers revealed that surface temperatures in the Red Sea may be cooling rather than rising. Analysis of long-term data sets shows that the current high warming rates of the Red Sea appear to be a combined effect of global warming and natural long-duration changes in sea surface temperature. "Our study has revealed a sequence of alternating positive and negative trends in Red Sea surface temperatures. Over the next decades, the trends indicate a cooling phase that may counter the effects of global warming," stated Ibrahim

Red Sea temperatures to cool in coming decades

1 min read ·
Exploring the links between natural climate cycles and the sea-surface temperature of the Red Sea reveals a cooling trend during the next few decades.

KAUST professors receive Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Mohamed Eddaoudi, KAUST distinguished professor of chemical science and director of the University's Advanced Membrane & Porous Materials Center, recently received the Abdul Hameed Shoman Award for Arab Researchers for Renewable Energy, an award presented annually by the Abdul Hameed Shoman Foundation. Omar Knio, KAUST professor of applied mathematics and computational science, was also honored by the Abdul Hameed Shoman Foundation this year as a joint recipient of the Shoman Award for Mathematical Modeling with Dr. Shaher Mohammed Ahmad Momani.

Sören Wolfers

Sören Wolfers obtained a Ph.D. degree in Applied Mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sören's research interests included Numerical Analysis, Uncertainty Quantification, Sparse Approximation, Multilevel methods and Meshfree methods. Selected Publications Beck, J., Roberts, G., and Wolfers, S. (2018). Bayesian earthquake dating and seismic hazard assessment using chlorine-36 measurements (BED v1). Submitted to

Saja Almohammadi

Saja Almohammadi is a PhD candidate in Applied Mathematics and Computational Sciences. She is studying under supervision of Professor Omar Knio. Research Interest Saja Almohammadi's research interest include working on Uncertainty Quantification (UQ) on chemical system. Development of methods for forward UQ as applied to chemical kinetic application relevant to Rapid compression machine on iso-octane. Education Profile M.Sc. in Applied Math, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2014. BS Mathematics Department, King Abdelaziz University (KAU), Saudi Arabia

Raul Tempone

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Quan Long

Research Scientist (former), Stochastic Numerics Research Group
Quan Long worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Quan obtained his Ph.D. degree in computational structural mechanics from CSMLab at Cambridge University, where his research focused on isogeometric analysis using subdivision techniques and its applications in large structural deformations, nematic elastomers, and dynamic fractures. During his postdoctoral research, he extended his interests from deterministic problems to stochastic problems, for example, Bayesian

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Marco Ballesio

Marco Ballesio is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Probability Theory and Stochastic Processes, Stochastic Modeling, Uncertainty Quantification. Education Profile Laurea Magistrale (Master’s Degree) in Mathematics for Engineering, Politecnico di Torino (Grade: 110/110 cum laude), (2013-3/2016). Allievi Honors Program, Allievo Senior, Collegio Carlo Alberto (2013-3/2016). Erasmus Student at Aalto

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.

Evangelia Kalligiannaki

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

Erik von Schwerin

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group
André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, ​the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications​ A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in
<> Computer science (21)

KAUST Research Conference on Mathematical and Data Sciences

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KAUST Campus

Theory and applications of data science.

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

Bringing computational engineering into the fold

1 min read ·
An interactive computational tool opens new possibilities for "developable" surfaces.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

Technology search for relationships

1 min read ·
A sketch-based query for searching for relationships among objects in images could enhance the power and utility of image search tools.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Simple statistics can be good enough

1 min read ·
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.

Mastering a prickly problem in ferrofluids

1 min read ·
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Watching viscous flow, but faster

1 min read ·
A state-of-the-art method for modeling the behavior of liquids described by the Computational Sciences Group represents a breakthrough in computational speed for viscous liquids.
<> modeling (20)

Houcem Ben Salem

Intern (former), Information Science Lab
Research Intern at KAUST(King Abdullah University of Science and Technology) .

2018 Workshop on Statistics and Data Science

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Auditorium 0215 between Buildings 4 & 5

The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

On mean-field game price models

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B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

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B9 L2 H1 R2322

This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

Modeling and Numerics of Sorption Kinetics

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B1 L3 R3119

An efficient method is proposed for the numerical solution of the Stokes equations in a domain with a moving bubble and two techniques for the treatment of the boundary conditions are adopted and then compared. The treatment of diffusion of surfactants (anions and cations) in presence of an oscillating bubble is an interesting interdisciplinary problem, with applications to chemistry and biology.

Professor Meriem Laleg's research on modeling contaminant transport published in SIAM Journal on Scientific Computing

1 min read ·
A new algorithm is proposed to estimate the average velocity, dispersion coefficient, and differentiation order of a space-fractional advection-dispersion equation used for modeling contaminant transport in porous media. This allows for the characterization of the medium and the determination of the contaminant source. The algorithm is efficient, robust and fast.

Professor Meriem Laleg's research on brain response models published in Journal of Neuroscience Methods

1 min read ·
A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements.

Professor Meriem Laleg's research on membrane distillation modeling for desalination published in Desalination

1 min read ·
An accurate mathematical model is proposed to describe an emerging desalination technology called direct contact membrane distillation system. The mathematical model is important for designing efficient control and monitoring strategies, a crucial step to facilitate the commercialization of this technology.

More rain for the Red Sea if El Niño breezes in

1 min read ·
Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

The Indian summer monsoon keeps the Arabian Peninsula hot and dry

1 min read ·
Modeling shows that the Indian summer monsoon can trigger heatwaves and sandstorms on the Arabian Peninsula.

KAUST Associate Professor Taous-Meriem Laleg-Kirati finalist at Leadership Excellence for Women Awards & Symposium

1 min read ·
Taous-Meriem Laleg-Kirati, KAUST associate professor of electrical engineering in the University's Computer, Electrical and Mathematical Science and Engineering Division, was selected as one of three finalists in the academic of distinction category at the recent Leadership Excellence for Women Awards & Symposium (LEWAS) in Bahrain.

Faculty Focus: Taous-Meriem Laleg-Kirati

1 min read ·
-By A. Ortega, KAUST News KAUST Associate Professor of Electrical Engineering Taous-Meriem Laleg-Kirati joined the University in 2011 after working as a researcher at INRIA in France. Laleg-Kirati leads the Estimation, Modeling and ANalysis (EMAN) research group at KAUST, and her interests lie in control theory, system modeling and signal applications. She investigates multiple engineering disciplines and develops mathematical models and algorithms that control processes relying on real-time feedback. "My research goals [are the development of] effective estimation methods and algorithms to

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Watching viscous flow, but faster

1 min read ·
A state-of-the-art method for modeling the behavior of liquids described by the Computational Sciences Group represents a breakthrough in computational speed for viscous liquids.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.
<> PDEs (20)

Extrapolated Linear Multistep Methods

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B9 L2 R2325

In this talk, we will discuss how linear multistep methods and classical extrapolation can be combined to obtain new classes of efficient time-integration methods.

Lajos Lóczi

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

Mathematical Modelling of Solar Cells

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B1 L3 R3119

This talk presents the development and the numerical approximation of mathematical models for some well known solar cell architectures.

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Stochastic Interacting Particle Methods and Generative Learning for Multiscale PDEs

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B1 L3 R3119

This talk discusses stochastic interacting particle (SIP) methods for advection-diffusion-reaction PDEs based on probabilistic representations of solutions, and shows their self-adaptivity and efficiency in several space dimensions.

Leader-Follower Opinion Dynamics: Socio-Economic Factors and Optimal Control

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B1 L3 R3119

This talk will discuss the partial differential equation-constrained optimal control for a leader-follower opinion formation model and derive first-order optimality conditions.

Juan Pablo Bernal Tamayo

AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory

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B9 L3 R3120

This talk describes a novel machine learning algorithm, inspired by complex analysis and dynamical systems theory, that significantly improves efficiency in solving partial differential equations and scientific computing, while providing a theoretical framework for reconstructing complex, unknown systems from partial observational data.

New Challenges in the Kinetic Theory of Complex Fluids

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B1 L4 R4214

This seminar presents a new kinetic theory for complex fluids, detailing the development of a Boltzmann-type equation and discussing its theoretical properties concerning relaxation to equilibrium, hydrodynamic limits, and a novel energy functional for calamitic fluids.

Towards Finite Element Tensor Calculus

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B1 L3 R3119

In this talk we provide an overview of some efforts to develop finite elements for tensors, motivated by the need for structure-preserving and compatible discretization.

Randomized Greedy Algorithms for Neural Network Optimization in Solving Partial Differential Equations

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B4 L5 R5220

This thesis introduces the randomized orthogonal greedy algorithm (ROGA) to bridge the gap between theoretical and practical performance of shallow neural networks for solving partial differential equations by overcoming key optimization challenges to achieve provably optimal convergence rates.

Dimension Liftings for Quantum Computation of Partial Differential Equations and Related Problems

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B1 L3 R3119

This lecture presents a systematic framework for developing quantum simulation algorithms for general differential equations, using dimension lifting to transform nonlinear and time-dependent problems into forms solvable by simulating the Schrödinger equation.
Analysis and PDEs Workshop-2025

Workshop on Analysis and PDEs

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B3, L5, R5220

Join us for the Workshop on PDEs to explore advances, challenges and methods in the analysis and computation of partial differential equations.

Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

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B1 L3 R3119

This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.
KAUST-CEMSE-NumPDE-Workshop-2024

NumPDE Workshop: Numerical Analysis of PDEs

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Sunday 26/01 (morning, 8.45-13.30) - Auditorium between Building 2 and Building 3; Sunday 26/01 (afternoon, from 13.45) - Building 9, Room 2322 (Lecture Hall); Monday 27/01 and Tuesday 28/01 - Auditorium between Building 4 and Building 5

Explore cutting-edge numerical analysis of PDEs, including Finite Element Methods, Virtual Element Methods, and Isogeometric Analysis, with leading experts.

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

On Some PDEs with singular data

Prof. Mohammed Kbiri Alaoui, King Khalid University

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B1 L3 R3119

The aim of this talk is to investigate the following non local p-Laplacian problem with data a bounded Radon measure.

Device accelerated solvers for PDEs. Current status and future perspectives

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B5 L5 R5220

In the next months, KAUST expects to place into service Shaheen-3, a supercomputer with a GPU partition whose planned 1 Exaflop/s HPL AI capability would rank it in the Top 6 globally if delivered today.

Umberto Zerbinati

Jinchao Xu

Professor Xu is widely recognized as a leading figure in developing, designing and analyzing fast methods for finite element discretization and large-scale equation solutions.
<> computational science (19)

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

KAUST Research Conference Digital Health 2020

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B19 L2 H1

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Low Rank Everywhere (Almost)

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B9 L2 R2322

Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Bringing computational engineering into the fold

1 min read ·
An interactive computational tool opens new possibilities for "developable" surfaces.

Faster prediction of wireless downtime

1 min read ·
An efficient simulation scheme that hones in on the rarest elements in a dataset can help predict capacity exceedances in wireless networks.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Technology search for relationships

1 min read ·
A sketch-based query for searching for relationships among objects in images could enhance the power and utility of image search tools.

Tunable sound transmission shapes up

1 min read ·
Carefully designed crystal structures can now be tuned to control how they transmit acoustic waves.

Taking graphics cards beyond gaming

1 min read ·
A highly efficient mathematical solver designed to run on graphics processors gives scientists and engineers a powerful new tool for a common computational problem.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Tiny speakers break a barrier for sound

1 min read ·
A loudspeaker design could allow small devices to produce powerful low-frequency sounds.

Making loudspeakers resonate like atoms

1 min read ·
Tiny loudspeakers can efficiently produce low-frequency sounds due to effects similar to the quantum physics governing atoms.

Better racing car design through an industry partnership

1 min read ·
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Quieter wind beneath the wings

1 min read ·
The ability to efficiently simulate the noise generated by wings and propellers promises to accelerate the development of quieter aircraft and turbines.

Georgiy L. Stenchikov

Emeritus Professor (former), Earth Science and Engineering
Georgiy L. Stenchikov, a former KAUST faculty member, is renowned for his work in climate modeling, atmospheric processes, and volcanic impacts.
<> mean-field games (19)

Part 3: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems.

Part 1: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 1: I will show how to transform suitable mean-field game (MFG) systems into infinite-dimensional convex optimization problems. Furthermore, I will present Uzawa’s algorithm and augmented Lagrangian approach for solving convex optimization problems. Finally, I will demonstrate how to apply these methods to approximate solutions of corresponding MFG systems.

Part 2: Variational structures and Fourier approximation techniques for mean-field game systems

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B1 L3 R3119

Mean-field game (MFG) systems of partial differential equations (PDE) arise in modeling huge populations of indistinguishable rational agents that play non-cooperative differential games. Mathematically, an MFG system comprises of a Hamilton-Jacobi-Bellman PDE coupled with a Kolmogorov-Fokker-Planck PDE in a highly nonlinear fashion. Hence, theoretical and numerical treatments of MFG systems are highly challenging problems. Day 2: I will present new Fourier approximation techniques for nonlocal MFG systems and discuss how these techniques fit the variational framework presented on Day 1. Then I will introduce the Chambolle-Pock primal-dual hybrid gradient optimization method and apply a variant of this method to approximate nonlocal MFG models via Fourier approximation techniques above.

Stationary Mean-Field Games with Congestion

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B2 L5 R5220

Mean-field games MFG are models of large populations of rational agents who seek to optimize an objective function that takes into account their state variables and the distribution of the state variable of the remaining agents. MFG with congestion model problems where the agents’ motion is hampered in high-density regions. First, we study radial solutions for first- and second-order stationary MFG with congestion on R^d. Next, we consider second-order stationary MFG with congestion and prove the existence of stationary solutions. Additionally, we study first-order stationary MFG with congestion with quadratic or power-like Hamiltonians.

On mean-field game price models

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B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

Two-Scale Homogenization and Numerical Methods for Stationary Mean-Field Games

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KAUST

Mean-field games (MFGs) study the behavior of rational and indistinguishable agents in a large population. Agents seek to minimize their cost based upon statistical information on the population's distribution. In this dissertation, we study the homogenization of a stationary first-order MFG and seek to find a numerical method to solve the homogenized problem. More precisely, we characterize the asymptotic behavior of a first-order stationary MFG with a periodically oscillating potential. Our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems. Moreover, we prove the existence and uniqueness of the solution to these limit problems. Next, we notice that the homogenized problem resembles the problem involving effective Hamiltonians and Mather measures, which arise in several problems, including homogenization of Hamilton--Jacobi equations, nonlinear control systems, and Aubry--Mather theory. Thus, we develop algorithms to solve the homogenized problem, effective Hamiltonians, and Mather measures.

Mean Field Games: Numerical Methods and Applications

Mathieu Laurière, Postdoc, Department of Operations Research and Financial Engineering, Princeton University

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KAUST

In this talk, several numerical methods will be presented and illustrated on examples. Borrowing tools from stochastic analysis, optimization, partial differential equations and machine learning, these methods enable us to solve mean field games with possibly complex sources of noise or high dimensional state variables.

Mean-Field Games on Networks and Wardrop Equilibria - Graduate Seminar

Ricardo De Lima Ribeiro, Research Specialist, CEMSE, KAUST

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B9 L2 R2322

Models for flows on networks arise in the study of traffic and pedestrian crowds. These models encode congestion effects, the behavior and preferences of agents, such as aversion to crowds and their attempts to minimize travel time. We will present the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary mean-field game.

Numerical approximations of Mean-Field-Games

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B1 L2 R2202

In this thesis, we present three projects. First, we investigate the numerical approximation of Hamilton-Jacobi equations with the Caputo time-fractional derivative. We introduce an explicit in time discretization of the Caputo derivative and a finite-difference scheme for the approximation of the Hamiltonian. We show that the approximation scheme is stable under an appropriate condition on the discretization parameters and converges to the unique viscosity solution of the Hamilton-Jacobi equation.

Mean Field Games price formation models

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B1 L4 R4214

This thesis studies mean-field games (MFGs) models of price formation. The thesis focuses explicitly on a MFGs price formation model proposed by Gomes and Saúde. The thesis is divided into two parts. The first part examines the deterministic supply case, while the second part extends the model to incorporate a stochastic supply function. We explore different approaches, such as Aubry-Mather theory, to study the properties of the MFGs price formation model and alternative formulations using a convex variational problem with constraints. We propose machine-learning-based numerical methods to approximate the solution of the MFGs price formation model in the deterministic and stochastic setting.

Hessian-dependent Functionals: Mean Field Games, Free Boundaries and Improved Regularity

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B2 L5 R5220

Hessian-dependent functionals play a pivotal role in a wide latitude of problems in mathematics. Arising in the context of differential geometry and probability theory, this class of problems find applications in the mechanics of deformable media (mostly in elasticity theory) and the modelling of slow viscous fluids. We study such functionals from three distinct perspectives.

KAUST Ph.D. students win best paper awards at mean-field games conferences

1 min read ·
By Sonia Turosienski KAUST Ph.D. students David Evangelista and Xianjin Yang, who are supervised by Diogo Gomes, professor of applied mathematics and computational science, won best paper awards at conferences this summer for their work in mean-field game (MFG) theory. Mean-field games model and seek to explain the behavior of rational agents in a competitive environment and have been used in diverse areas of research, including studies on non-renewable resources and mining models; opinion dynamics; price impact and order book modeling; and networks and energy management. Evangelista was

Peter Markowich elected Fellow of the European Academy of Sciences

1 min read ·
KAUST Professor Peter Markowich has been elected as a Fellow of the European Academy of Sciences (EurASc). Established in 2003, the EurASC is a fully independent international organization of distinguished scholars that aims to recognize and elect to its membership the best European researchers to strengthen European science and scientific cooperation.

Professor Markowich elected to the European Academy of Sciences and Arts

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich has been named a member of the European Academy of Sciences and Arts (EASA). He will officially be inducted as a member at the organization's annual inauguration ceremony in early 2023.

Xianjin Yang

Xianjin Yang's research interests fall on mean-field games, which is an active research area in differential game theories. In his PhD period, Xianjin focuses on the homogenization and numerical methods for stationary mean-field games. Education and Early Career Xianjin Yang got his bachelor's degree in Software Engineering from Chongqing University of China in 2011. Then, he obtained his Master with majors in Computer Science from Zhejiang University of China in 2014. Later on, Xianjin Yang received his second Master's degree in Applied Mathematics from KAUST in 2016. He continues to study at

Peter Markowich

Al-Khawarzmi Distinguished Professor, Applied Mathematics and Computational Science
A distinguished professor of applied mathematics and computational science, Dr. Markowich is the author of over 200 publications in leading international journals and circa 14,000 citations.

Julian David Gutierrez Pineda

Julian received both B.S. in Mathematics and B.S. in Engineering from Escuela Colombiana de Ingenieria, Colombia. After receiving his M.Sc. in Mathematical Sciences from Universidad Nacional Autónoma de México, México, he joined Professor Diogo Gomes as a Ph.D. student in applied mathematics at King Abdullah University of Science and Technology, Saudi Arabia. His research interests are primarily optimal control, optimal transport, calculus of variations, partial differential equations, and mean-field games.

Diogo Gomes

Professor Diogo Gomes, a distinguished expert in Applied Mathematics and Computational Science at KAUST, leverages advanced mathematics – including partial differential equations, numerical methods and mean-field game models – to solve complex problems in social sciences, economics and finance

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research
<> scientific computing (16)

KAUST Research Conference on Mathematical and Data Sciences

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KAUST Campus

Theory and applications of data science.

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

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B9, L2, R2322

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory

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B9 L3 R3120

This talk describes a novel machine learning algorithm, inspired by complex analysis and dynamical systems theory, that significantly improves efficiency in solving partial differential equations and scientific computing, while providing a theoretical framework for reconstructing complex, unknown systems from partial observational data.
Georgios Grekas

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Georgios Grekas research focuses on mathematical modeling (biomechanics, solid mechanics), numerical analysis, scientific and parallel computing, and scientific machine learning.

Scalable Hierarchical Algorithms for eXtreme Computing Workshop

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KAUST

The 2012 SHAX-C workshop focuses international expert attention on the prospects for the three great hierarchical algorithms of scientific computing: multigrid, fast transforms, and fast multipole methods. These methods are kernels in simulations based on formulations of partial differential equations, integral equations, and interacting particles – in short, they are scientific and engineering workhorses.

Deep Learning and Scientific Computing

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B9 L2 R2322

In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Umberto Zerbinati

Theodoros Katsaounis

Dr. Theodoros research interests are in the area of applied and computational mathematics with emphasis on the development, analysis and implementation of numerical methods for approximating solutions of PDE’s modelling various physical processes.

Steven Dufour

Steven Dufour is a Former Visiting ​Associate Professor from École Polytechnique de Montréal, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Steven's research activities revolve around the development of numerical methodologies for the modeling of free surface flows that can be found in various industrial applications. The main challenge associated with the numerical modeling of multifluid flows is to accurately locate the interfaces between each fluid. More complex flows, such as the flow of

Jorge Alejandro Amador Herrera

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic

Grigorios Kounadis

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Short Bio Grigorios Kounadis is a Postdoctoral Research Fellow at the Applied PDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. He received a bachelor degree in Mathematics from National and Kapodistrian University of Athens (NKUA), Greece, in 2006, and graduated with a Ph.D degree in Applied Mathematics from NKUA in 2020. He joined KAUST and the Applied PDE group in March 2021. Grigorios' research interests lie in the area of numerical analysis and scientific computing

George Turkiyyah

Professor Turkiyyah's expertise lies in high-performance and GPU computing, numerical simulation and optimization and their application to machine learning and large-scale modeling.

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Carlos Andrés Muñoz Moncayo

Carlos Andrés Muñoz Moncayo is an M.S./Ph.d. candidate in applied mathematics and computational sciences program and a member of The Numerical Mathematics Group under the supervision of Professor David Ketcheson at King Abdullah University of Science and Technology (KAUST). Research Interests Carlos' research interests include partial differential equations, numerical analysis, and scientific computing.
<> AI (15)

KAUST, in collaboration with MCIT, to host the second edition of the MENA Machine Learning Winter School (MenaML)

1 min read ·
A week of advanced instruction and exchange for emerging machine learning talent.

Data-driven Anomaly Detection in Industrial Processes

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B9 L2 R2325

This talk presents a model-based anomaly detection framework, along with data-driven process monitoring approaches based on multivariate statistical methods and artificial intelligence techniques.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Mathematical Imaging in the Era of AI

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B1 L23 R3119

In this talk, I will present mathematical imaging as a meeting point of classical analysis and modern AI. I will highlight how ideas from optimisation and mathematical modelling can guide the development of structure-preserving deep learning methods, offering new, principled approaches to large-scale inverse imaging problems.

Nuclear Fusion Powered by AI and HPC

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B9 L2 R2325

This talk presents advanced simulations with AI-driven optimization to improve the performance of a next-generation plasma-jet-driven magneto-inertial fusion concept.

AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory

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B9 L3 R3120

This talk describes a novel machine learning algorithm, inspired by complex analysis and dynamical systems theory, that significantly improves efficiency in solving partial differential equations and scientific computing, while providing a theoretical framework for reconstructing complex, unknown systems from partial observational data.

AI for Science: AI-Driven Knowledge Discovery

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B1 L3 R3119

This talk focuses on the latest progress in this area, often called AI for Science, and specifically on how AI is revolutionizing scientific exploration and the uncovering of fundamental physical laws.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.
kaust-iit-collaboration-life-sciences-nanotechnology-research

KAUST collaboration to benefit life sciences and nanotechnology research

1 min read ·
KAUST and IIT unite to enhance research in life sciences, AI, robotics and nanotech, driving innovation through collaboration.

Aznaur Aliev

M.S. Student

Mhd. Emad Alolaby

Mohammed A Shukri

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

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B3 L5 R5209

Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems
<> big data (15)

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Big Data Analyses in Evolutionary Biology

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B9 H2

This event is organized by CBRC with financial support from the KAUST Office of Sponsored Research

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Exploring science's fourth paradigm

1 min read ·
The recent KAUST Research Conference: Computational and Statistical Interface to Big Data brought together leading computer science researchers and statistical experts to discuss the current state and future of data science. Held on campus from March 19 to 21, the conference covered such data science topics as succinct data representation and storage; big data visualization; parallel and distributed algorithms for inference and optimization; and analysis of large graphs and networks.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Getting the inside track on street design

1 min read ·
Large-scale analysis of pedestrian data from three European cities examines how building density and street design influence pedestrian behavior.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Star light, star bright…as explained by math

1 min read ·
The evolving periodicity of the brightness of certain types of stars can now be described mathematically.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

Competition sheds light on approximation methods for large spatial datasets

1 min read ·
Marc Genton, Huang Huang and colleagues from KAUST organized a global competition with 21 competing teams to compare different approximation methods for analyzing large spatial datasets.

Decrypting the interdependence of cryptocurrencies

1 min read ·
A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

Code of collaboration

1 min read ·
David Keyes's and Matteo Ravasi's cross-disciplinary project results in improved efficiency for seismic processing, with promising applications for the energy industry.

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.
<> earth science and engineering (15)

Securing future energy supply

1 min read ·
KAUST’s Earth Science and Engineering Program is underpinned by a drive to develop sustainable ways to get the best from natural resources and quantify environmental hazards.

Reconstructing the Red Sea's climate patterns

1 min read ·
An advanced numerical model is helping researchers better understand the variability of the Red Sea’s climate patterns.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Winds and waves winding down

1 min read ·
Wind intensities and wave heights in the Red Sea are decreasing over time—potential for a significant impact on its ecosystem.

More rain for the Red Sea if El Niño breezes in

1 min read ·
Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

Diagnosing the impacts of health policy

1 min read ·
Study design allows more rigorous evaluation of complex healthcare interventions.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

Red Sea flushes faster from far-flung volcanoes

1 min read ·
Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Simulating Red Sea water exchanges

1 min read ·
Powerful computer simulations are revealing new insights into water exchanges between the Red Sea and the Gulf of Aden.

West Africa warms but airborne dust keeps the Red Sea cool

1 min read ·
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Remote sensing of toxic algal blooms

1 min read ·
Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

Georgiy L. Stenchikov

Emeritus Professor (former), Earth Science and Engineering
Georgiy L. Stenchikov, a former KAUST faculty member, is renowned for his work in climate modeling, atmospheric processes, and volcanic impacts.
<> High Performance Computing (15)

New cost-effective GPUs solver redesigns the computational game

1 min read ·
Prof. David Keyes and his team proposed a GPUs light architecture to solve systems of multiple equations involving a large amount of data. Broadly used in computer gaming, mobile, and PC graphics - GPUs allow customizing an effective result-driven support to create a computational framework for increasing the number of processors while reducing the memory required to temporarily store the data.

Pulling rank on spatial statistics

1 min read ·
A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

KAUST gets ready for SC18

1 min read ·
For the past 10 years, KAUST has made a strong presence at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) that takes place in different cities around the U.S. every year. 2018 will mark a special year for KAUST at SC, which will take place in Dallas, Texas, in November, with around 13,000 supercomputing professionals in attendance. The University will have an engaging presence on the exhibition floor and in the seminars, and the technical program of SC18 will be chaired by the Director of the KAUST Extreme Computing Research Center (ECRC)

KAUST Ph.D. student wins best paper award from American Statistical Association

1 min read ·
KAUST Ph.D. statistics student Jian Cao was recently selected as a best paper award winner by the American Statistical Association (ASA) for his paper entitled "Computing High-Dimensional Normal and Student-t Probabilities with Tile-Low-Rank Quasi-Monte Carlo and Block Reordering." Cao's paper was chosen in an ASA student paper competition under the section on Statistical Computing.

Radouan Boukharfane

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Radouan Boukharfane joined Extreme Computing Research Center (ECRC) at King Abdullah University of Science & Technology (KAUST) as a postdoctoral fellow in 2019. He is working with Professor Matteo Parsani his research group. Prior to this, he spent a year as a Postdoctoral Research Fellow in the Department of Aerodynamics, Energetics and Propulsion (DAEP) of the National Higher French Institute of Aeronautics and Space (ISAE-SUPAERO) in Toulouse (France). Education and Early Career Dr. Boukharfane received a joint MSc. (2014) from Department of mechanical engineering at Ecole Polytechnique of

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Mohammed Sayyari

Mohammed is a Mathematician with a solid background in scientific programming. Mohammed's mathematical interests are in the analysis of partial differential equations and the discretization thereof.
KAUST-CEMSE-Matteo-Parsani

Matteo Parsani

Professor Parsani develops robust, variable order and self-adaptive algorithms for solving fluid flow problems in complex geometries. The application domains currently driving Parsani's research are compressible computational aerodynamics, computational aeroacoustics for noise reduction in vehicles and molecular communication.

Irving Enrique Reyna Nolasco

Research Interests Irving Enrique Reyna Nolasco's research interests include numerical methods for partial differential equations, large eddy simulations, high order entropy stable schemes, machine learning, complex systems, and non-linear dynamics. Why did you choose your field of research? As a graduate student, I have always found satisfaction in developing scientific software and libraries for the simulation and visualization of different phenomena. I enjoy applying scientific computing tools and mathematical concepts to relevant problems in physics. By the end of my bachelor studies, I

Hatem Ltaief

Principal Research Scientist, Applied Mathematics and Computational Science
Dr. Hatem Ltaief is a Principal Research Scientist at KAUST. He works on mixed-precision algorithms and performance optimizations to help HPC scientific applications address the exascale challenges. His innovations have been recognized with the ACM Gordon Bell Prize (shared) for Climate Modeling in 2024.

Habib Toye Mahamadou Kele

Brief Biography Habib Toye is a PhD candidate at King Abdullah University of Science and Technology (KAUST), in the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) under the supervision of Professor Ibrahim Hoteit. His research interests include Ensemble ocean forecasting, High performance computing and Data assimilation. Research Interest Habib's research focuses on developing and implementing efficient ensemble Kalman filters for data assimilation into a high resolution model of the Red Sea. He has developed a fault-free DART-MITgcm ensemble assimilation

George Turkiyyah

Professor Turkiyyah's expertise lies in high-performance and GPU computing, numerical simulation and optimization and their application to machine learning and large-scale modeling.

Dalal Sukkari

Research interests and present research project. Dalal's research centers on a new high performance implementation of the QR-based Dynamically Weighted Halley iterations (QDWH) to compute the polar decomposition and its application to the SVD (QDWH-SVD). She has introduced a high performance QDWH-SVD implementation on multicore architecture enhanced with multiple GPUs, and on distributed memory based on the state-of-the-art vendor-optimized numerical library ScaLAPACK, and has presented the first asynchronous, task-based formulation of the polar decomposition QDWH and its corresponding

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Aimad ER-RAIY

Aimad Er-Raiy joined the Extreme Computing Research Center (ECRC) at King Abdullah University of Science & Technology (KAUST) as a postdoctoral fellow in late 2019. He is working with Professor Matteo Parsani. Prior to this, he spent a year as a Postdoctoral Research Fellow in CERFACS (Toulouse, France). Education and Early Career Dr. Er-Raiy received a MSc. (2014) from the National Institute of Posts and Telecommunications (INPT) in Toulouse, France, and a Ph.D. (2018) from Institut Pprime & National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA) in Poitiers (France). Research
<> algorithms (14)

Mathematical Design and Analysis of Iterative Methods for Linear and Nonlinear Problems

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B1 L3 R3119

This talk presents a systematic approach to the design and analysis of iterative methods for solving linear and nonlinear problems.

Success and Challenges of Computational Fluid Dynamics for Engineering Applications

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B1 L3 R3119

The talk will introduce an efficient, accurate, and robust Computational Fluid Dynamics (CFD) software package utilizing a finite-volume algorithm on structured multiblock grids to simulate challenging three-dimension unsteady turbulent flows for multi-disciplinary applications like aerodynamics, aeroelasticity, and combustion, showcasing its successes and remaining challenges in large-eddy simulations for complex reactive flows.

Towards Flexible Polyhedral Nets

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B4 L5 R5209

This thesis research provides a comprehensive classification of flexible geometric nets of arbitrary size in both Euclidean and isotropic geometries, revealing that only two distinct classes exist in each setting and demonstrating their relationship to mechanical design.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Estimation Problems for Partial and Fractional Differential Equations - Autumn 2019

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B9 L2 H1 R2322

I will present an overview of our activities around estimation problems for partial and fractional differential equations. I will present the methods and the algorithms we develop for the state, source and parameters estimation and illustrate the results with some simulations and real applications.

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Proximal Algorithms for Large-Scale Convex Non-smooth Optimization

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B2 L5 R5220

Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Alibek Sailanbayev

Alibek Sailanbayev is a Ph.D. candidate in Applied Mathematics and Computational Science at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Alibek obtained his bachelor degree Applied Mathematics from Nazarbayev University in Kazakhstan. He joined KAUST in Fall 2017 and received his M.S. degree in Applied Mathematics and Computational Sciences in December 2018. He is now pursuing Ph.D. studies. Alibek took a Summer Internship at Loria/Inria
<> Environmental Statistics (14)

Second year Ph.D. student to receive top statistics award

1 min read ·
KAUST second year Ph.D. student Yuxiao Li has been selected to receive a Student Paper Award from the American Statistical Association (ASA) for his paper entitled "Efficient Estimation of Non-stationary Spatial Covariance Functions with Application to High-resolution Climate Model Emulation."

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.

Taking aim at outliers

1 min read ·
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.

Honing in on hotspots in spatial data

1 min read ·
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

Expanding the scale of dangerous weather prediction

1 min read ·
A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.

Shaking up the landslide model

1 min read ·
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.

CEMSE Division student spotlight: Gaurav Agarwal

1 min read ·
Gaurav Agarwal firmly believes in the age-old adage that patience is a virtue. Without patience, life’s challenges cannot be overcome, and a steadfast belief in perseverance has served the statistics Ph.D. student well throughout his academic career.

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

Modeling rainfall drop by drop

1 min read ·
A high-frequency model developed using data from new high-precision rain gauges gives fresh insight into the dynamics of rain and runoff events.

Getting the tail straight

1 min read ·
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Professor Marc Genton receives the prestigious 2024 Don Owen Award

1 min read ·
KAUST Professor Marc Genton, a pioneer in environmental statistics, was awarded the prestigious 2024 Don Owen Award for his outstanding contributions to research, consultation, and service to the statistical community.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Gaurav Agarwal

Ph.D., Statistics
Gaurav Agarwal obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. His research focused on developing statistical models for dealing with the issues concerning the environment. Bringing data to life, with statistics and programming excites him. Besides research, he likes to dance and work out. Research Interest Gaurav's research interests included Multivariate quantiles, Non-parametric statistics, Functional data analysis, and Spatial Statistics. Awards and
<> marine science (14)

Unlocking the mysteries of Red Sea eddies

1 min read ·
First-time reports of the statistical properties of ‘whirlpools’ show they are frequent and seasonal.

Coral reef fish: not such a family affair

1 min read ·
Some coral reef fish species don’t settle very close to home.

Assessing seawater safety

1 min read ·
Elevated bacterial populations in waters near beaches and urban areas could pose a problem for desalination plants.

The Red Sea models the future

1 min read ·
Detailed analysis of nutrient distribution and circulation in the Red Sea could provide a model for the future of the world’s oceans.

An ocean observatory for the Red Sea

1 min read ·
Studies conducted at the Saudi Aramco-KAUST Marine Environmental Research Center provide new insights into the physical and biological aspects of the Red Sea.

Feeding in the moonlight

1 min read ·
Consistent around the world's ocean, the phases of the moon affect the upward migration of the world’s most abundant type of fish for feeding.

Whale shark populations are young and transient

1 min read ·
A population study of whale sharks in the Red Sea reveals unique group dynamics.

Assessing Red Sea potential for alternative energy

1 min read ·
Advanced numerical models are helping researchers identify potential sites to exploit offshore wind and wave energy in the Red Sea region.

Microbial communities have a seasonal shake-up

1 min read ·
Turbulence and nutrient availability drive changes in Red Sea microbes.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

Mysterious gentle giants gather off Saudi coast

1 min read ·
Investigations of prey patterns fail to explain why whale sharks aggregate off the coast of Saudi Arabia.

Red Sea flushes faster from far-flung volcanoes

1 min read ·
Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Remote sensing of toxic algal blooms

1 min read ·
Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.
<> climate science (13)

Letting the data speak for itself

1 min read ·
A new statistical approach for environmental measurements lets the data determine how to model extreme events.

Challenging statistics of weather extremes

1 min read ·
More accurate statistical modeling of extreme weather will improve forecasting and disaster mitigation.

Later tropical blooms could affect marine life

1 min read ·
Color changes in the northern Red Sea indicate rising sea temperatures could significantly impact tropical marine ecosystems.

The Indian summer monsoon keeps the Arabian Peninsula hot and dry

1 min read ·
Modeling shows that the Indian summer monsoon can trigger heatwaves and sandstorms on the Arabian Peninsula.

Chasing spatial patterns

1 min read ·
A more accurate way of resolving spatial patterns in weather could lead to better predictions of climate change.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

West Africa warms but airborne dust keeps the Red Sea cool

1 min read ·
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

A sharper view of flood risk

1 min read ·
Extreme weather patterns and regions at risk of flooding could be easier to spot using a new statistical model for large spatial datasets.

Blueprint may power up KSA's wind energy future

1 min read ·
High-resolution analysis of wind speed across Saudi Arabia can help fast track the expansion of the Kingdom’s emerging world-class wind energy industry.

Wind forecasts power up for reliable energy production

1 min read ·
Prediction of wind speed and direction for up to several hours in advance improves Saudi Arabia’s wind energy potential.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.

Finding Terra Incognita

1 min read ·
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.
<> Mathematical modeling (13)

Mathematical Modelling of Solar Cells

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B1 L3 R3119

This talk presents the development and the numerical approximation of mathematical models for some well known solar cell architectures.

C1+alpha Regularity for the Fractional p-Laplacian

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B9 L2 R2325

In this talk, we will discuss a recent result obtained in collaboration with Davide Giovagnoli and Luis Silvestre, where we solve a standing conjecture asserting that, as in the local setting, solutions of the homogeneous equation are still Hölder differentiable.

Collective Behaviors - From Traffic Congestion to Chase-and-Escape

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B5 L5 R5220

In this lecture, we introduce representative mathematical models of collective motion, showing how they capture unexpected emergent patterns and discuss chase-and-escape dynamics, a classic topic in mathematical modeling.

Ali O Balubaid

Ph.D Directed Research at KAUST (King Abdullah University of Science and Technology)

International Seminar on Mathematical Models and Numerical Methods for Flow and Transport in Porous Media

Speakers from KAUST, CEMSE, PSE, G-CSC, IBRAE and INM RAS

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KAUST

This workshop is devoted to numerical simulation of groundwater flow and subsurface contamination transport, as well as related problems. The main topics are mathematical modeling of the processes in porous media and the numerical methods for discretization, solution of the discretized systems and numerical treatment of inverse problems. In particular, fractured porous media and partially saturated aquifers will be concerned.

On the Mathematics of Turbulent flows

Luigi C. Berselli, Visiting Faculty, University of Pisa

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B9 L3 R3128

The understanding and description of turbulent flows poses several different problems in which theory, modeling, and (numerical) experiments interact in a multidisciplinary way. In this talk we present some of the basic tools, focusing on the mathematical foundation of the models used to describe the larger scales of the incompressible motions at high Reynolds number. Recent perspectives and open problems are also discussed.

Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

1 min read ·
Marc Genton, KAUST distinguished professor of statistics, and his former postdoctoral fellow Stefano Castruccio, have jointly won the 2017 Wilcoxon Award for best applications paper in Technometrics. Technometrics is a journal of statistics for the Physical, Chemical and Engineering Sciences, and is published quarterly by the American Society for Quality and the American Statistical Association. The award announcement was made at the 61st Annual Fall Technical Conference (FTC) in Philadelphia from October 5 to 6, 2017. Genton and Castruccio's paper was entitled "Compressing an ensemble with

Meet KAUST prospective student: Stefanos Georgiadis

1 min read ·
Stefanos Georgiadis is an applied mathematics graduate who will join KAUST from the School of Applied Mathematical and Physical Sciences, Athens, Greece. Stefanos will join KAUST this fall as a Ph.D. candidate and member of the Applied Partial Differential Equations (AppliedPDE) research group under the supervision of Professor Athanasios Tzavaras.

Waleed Alhaddad

Waleed Alhaddad obtained his Master's degree in Applied Mathematics and Computational Sciences from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Raul F. Tempone at Stochastic Numerics Research Group (STOCHNUM). Research Interests Waleed's research interests include ​Mathematical Modeling, Probability Theory and Stochastic Processes, Numerical Analysis. Education Profile ​M.Sc. Applied Mathematics, Finance, King Abdullah University of Science and Technology (KAUST) 2016-2018. B.Sc. Physics, University of California Santa Barbara 2016. Awards and

Theodoros Katsaounis

Dr. Theodoros research interests are in the area of applied and computational mathematics with emphasis on the development, analysis and implementation of numerical methods for approximating solutions of PDE’s modelling various physical processes.

Simone Portaro

Simone Portaro is a Ph.D. Student in the Computer, Electrical, Mathematical Sciences and Engineering (CEMSE) department at the Applied Mathematics and Computational Sciences (AMCS) program under the supervision of Professor Peter Markowich at King Abdullah University of Science and Technology. Simone received his B.S. degree in Industrial Engineering from the University of Catania, Italy in 2017. In 2020, he received his M.S. degree in Mathematical Engineering from Politecnico di Torino, Italy. Research interests Nonlinear Partial Differential Equations, Analysis-Modeling of biological network

Nadezhda Briantceva

Nadezhda is a Ph.D. student in the Advanced Simulations and Algorithms Lab (AANSLab) and the Communication Theory Lab (CTLab) under the supervision of Professor Matteo Parsani and Professor Mohamed-Slim Alouini. Education and Early Career Nadezhda obtained her bachelor degree in Applied Mathematics and Computational Science from Saint Petersburg State University (SPbSU) in 2018. She also has experience in commercial IT development as a QA engineer. Research Interest Nadezhda is focusing in the area of multi-agent systems, as well as mathematical modelling and optimization. Education Profile Ph

Håkon A. Hoel

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations
<> PDE (13)

Part 1: Introduction to homogenisation and Gamma-Convergence

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B2 B3 A0215

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 1: (1) The homogenisation problem; (2) Gamma-convergence - definition, main properties, examples, comparison with pointwise limit; (3) Lower semicontinuity, lower semicontinuous envelope; (4) Coercivity

Part 2: Introduction to homogenisation and Gamma-Convergence

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B1 L2 R2202

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 2: (1) Integral functionals with p growth, 1 p 1; The localisation method of Gamma-convergence; Integral representation.

Part 3: Introduction to homogenisation and Gamma-Convergence

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B1 L4 R4214

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 3: (1) Compactness; (2) Application of the compactness result; 93) The homogenisation theorem.

A very short introduction to regularity theory for linear elliptic equations - Part 1

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 1 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 3

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 3 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 4

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 4 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 2

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L2 R2202

Mini Course Part 2 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Unveiling the potential of energy/entropy stable numerical methods for hyperbolic/mixed hyperbolic-parabolic PDEs

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B9 L2 H1

Abstract The demand for increasingly multidisciplinary reliable simulations, for both analysis and design optimization purposes, requires transformational advances in individual components of future PDEs solvers. At the algorithmic level, hardware compatibility and efficiency are of paramount importance in determining viability on future hardware. However, equally important (if not more so) is provable algorithmic robustness which becomes progressively more challenging to achieve as problem size and physics complexity increase. We show that rigorously designed adaptive semi- and fully-discrete

Deterministic particle approximation for nonlinear transport equations

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Building L3 R3119

Approximating the solution to an evolutionary partial differential equation by a set of "moving particles" has several advantages. It validates the use of a continuity equation in an "individuals-based" modeling setting, it provides a link between Lagrangian and Eulerian description, and it defines a "natural" numerical approach to those equations. I will describe recent rigorous results in that context. The main one deals with one-dimensional scalar conservation laws with nonnegative initial data, for which we prove that the a suitably designed "follow-the-leader" particle scheme approximates entropy solutions in the sense of Kruzkov in the many particle limit. Said result represents a new way to solve scalar conservation laws with bounded and integrable initial data. The same method applies to second order traffic flow models, to nonlocal transport equations, and to the Hughes model for pedestrian movements.

WAN Approximation of PDEs: Best Approximation, Stabilization and Boundary Conditions

Prof. Silvia Bertoluzza

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B2 L5 R5209

We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.

Virtual Element Approximation of Eigenvalue Problems

Prof. Francesca Gardini, Università di Pavia

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B1 L3 R3119

We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.

Myoungkeun Shin

PDE UWOC

Myoungkeun Shin is a MS/Ph.D student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Myoungkeun obtained his bachelor degree in mathematics from Seoul National University (SNU) in 2011. Before joining KAUST, he taught mathematics to high school students in Korea. Research Interest Myoungkeun’s primary research interests are in PDE and UWOC. Education Profile M.S. in Applied Mathematics and Computational Science, Kind Abdullah University of Science and

David Ketcheson

Professor Ketcheson is an expert in numerical analysis and computational science, specializing in the study of numerical methods for differential equations and the modeling and analysis of time-dependent nonlinear wave phenomena.
<> supercomputing (13)

Top-ranked Arab university unveils Middle East’s most powerful supercomputer

1 min read ·
Shaheen III will unlock unparalleled opportunities to deliver impact in Saudi Arabia

Fifth KAUST-NVIDIA Workshop on Accelerating Scientific Applications Using GPUs

Timothy Lanfear , Brent Leback

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B4 B5 A0215

The KAUST Supercomputing Laboratory is co-organizing with NVIDIA, a leader in accelerated computing and artificial intelligence, a full-day workshop on accelerating scientific applications using GPUs on Tuesday, February 20th, 2018 in the auditorium between buildings 4 and 5.

Device accelerated solvers for PDEs. Current status and future perspectives

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B5 L5 R5220

In the next months, KAUST expects to place into service Shaheen-3, a supercomputer with a GPU partition whose planned 1 Exaflop/s HPL AI capability would rank it in the Top 6 globally if delivered today.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

KAUST gets ready for SC18

1 min read ·
For the past 10 years, KAUST has made a strong presence at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) that takes place in different cities around the U.S. every year. 2018 will mark a special year for KAUST at SC, which will take place in Dallas, Texas, in November, with around 13,000 supercomputing professionals in attendance. The University will have an engaging presence on the exhibition floor and in the seminars, and the technical program of SC18 will be chaired by the Director of the KAUST Extreme Computing Research Center (ECRC)

Next generation algorithm advances machine learning of powerful supercomputers

1 min read ·
A multi-disciplinary team of international researchers from KAUST and Japan's National Institute of Informatics (NII) in collaboration with U.S. supercomputer company Cray Inc. successfully implemented a new algorithm to efficiently harness the computational power of the fastest supercomputers in the world. "Software integration is ultimately the icing on the cake for advanced computational projects. Thanks to Cray's support, our algorithms are now deployed on all Cray supercomputers, including KAUST's Shaheen II supercomputer, as well as half of the top 10 fastest supercomputers in the world

KAUST Professor David Keyes to chair International Supercomputing Conference 2020

1 min read ·
David Keyes, KAUST professor of applied mathematics and computational science; director of the University's Extreme Computing Research Center; and senior associate to the President of KAUST, will chair the International Supercomputing Conference (ISC) 2020. He will be the first program chair from a Middle Eastern institution, as announced by ISC 2020.

West Africa warms but airborne dust keeps the Red Sea cool

1 min read ·
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.

Measuring solar stores of the Arabian Peninsula

1 min read ·
Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

Shaheen II open to serve COVID-19 research around the Kingdom

1 min read ·
The COVID-19 pandemic has imposed challenges across the globe that need to be solved using scientific discovery and innovation. In line with that, the KAUST Supercomputing Core Laboratory (KSL) has opened a call for proposals that require its supercomputer Shaheen II, as well as other cluster computing resources, to support research projects.

KAUST scientists win prestigious Gauss Award for supercomputing excellence

1 min read ·
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy, have won the German Gauss Center for Supercomputing (GCS) Award for original research that best advances high-performance computing. This makes KAUST the first Middle Eastern institution to receive this prestigious award.

KAUST presents exascale expertise at SC21

1 min read ·
Supercomputing experts from KAUST converged with global participants at the America's Center in St. Louis, Missouri from November 14 to 19 for SC21, the annual international conference for high performance computing (HPC), networking, storage and analysis. Established in 1988, the SC conference is the largest HPC event of its kind, with big name industries like Intel, Red Hat and Oracle sharing the exhibition floor alongside universities, government agencies and small businesses, and professionals from various affiliations attending presentations throughout the convention center. In non
<> Applied Partial Differential Equations (12)

On Nonlocal Keller-Segel Type Equations

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B1 L3 R3119

In the first part of the talk we investigate a Keller-Segel model with quorum sensing and a fractional diffusion operator. This model describes the collective cell movement due to chemical sensing with flux limitation for high cell densities and with anomalous media represented by a nonlinear, degenerate fractional diffusion operator. The purpose here is to introduce and prove the existence of a properly defined entropy solution. In the second part of the talk we will analyze an equation that is gradient flow of a functional related to Hardy-Littlewood-Sobolev inequality in whole Euclidean space of higher dimensions.

IVth KAUST MaS Workshop on Modelling and Simulation

speakers from KAUST, Birmingham, Graz, Utrecht, Stuttgart, Frankfurt, Buffalo, Linz, Weissach, Lugano, Kaliningrad, Heidelberg, State College, Philadelphia, Torino, Riyadh

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B3 L5 R5209

The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Models based on partial differential equations Novel models in biosciences Computational methods in empirical sciences. Numerical methods for partial differential equations Historical and scientific background Previous KAUST MaS workshops were held in 2017, 2018 and 2019 To download the Workshop's Agenda click here. Agenda Monday, 21 March 2022 Day 1 Saudi Arabia(AST) Speakers Description 10:00 Tony Chan (KAUST President) Opening Remarks 10:15 Gundolf Haase

5th KAUST CEMSE MaS Workshop on Modelling and Simulation

Speakers from KAUST, Melbourne, Utrecht, Karlsruhe, Erlangen, Brisbane, Langensteinbach, Lugano, Frankfurt, Italy

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B3 L5 R5209

The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Simulating unsteady CO 2 flow through brine-saturated cross-bedded sandstones: towards relative permeability curves for unstable displacement On Fourier methods and machine learning techniques in Computational Finance Pore-scale modelling of flow and transport in porous media Uncertainty Quantification of Density-Driven Groundwater Flow Simulation of unsaturated Density-Driven flow with a phreatic surface Modeling and Large-scale Simulation of Carbon Dioxide

Scalable parallel solvers for cardiac reaction-diffusion models and applications

Luca F. Pavarino, Professor, Department of Mathematics, Università degli Studi di Pavia

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B2 L5 R5220

After a brief introduction to the field of Computational Cardiology and cardiac reentry, we introduce and study some scalable domain decomposition preconditioners for cardiac reaction-diffusion models, discretized with splitting semi-implicit techniques in time and isoparametric finite elements in space.

Professor Peter Markowich Has Been Awarded The "John Von Neumann Visiting Chair" At The Technical University Of Munich

1 min read ·
Professor Peter Markowich has been awarded the "John Von Neumann Visiting Chair" at the Technical University of Munich. The John von Neumann Visiting Professorship allows outstanding scientists to spend a semester at the TUM Center for Mathematics.

Peter Markowich elected Fellow of the European Academy of Sciences

1 min read ·
KAUST Professor Peter Markowich has been elected as a Fellow of the European Academy of Sciences (EurASc). Established in 2003, the EurASC is a fully independent international organization of distinguished scholars that aims to recognize and elect to its membership the best European researchers to strengthen European science and scientific cooperation.

Professor Markowich named Fellow of the Institute of Mathematics and its Applications

1 min read ·
Prof. Peter Markowich has been named a Fellow of the Institute of Mathematics and its Applications (IMA). IMA Fellowships are awarded to individuals who have shown demonstrable achievement and competence in the development or application of mathematics.

Professor Athanasios Tzavaras elected Fellow of the European Academy of Sciences (EurASc)

1 min read ·
Athanasios (Thanos) Tzavaras has been elected Fellow of the European Academy of Sciences (EurASc). He was nominated for his contributions to the interface of nonlinear partial differential equations and applied mathematics in the physical sciences.

Professor Markowich elected to the European Academy of Sciences and Arts

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich has been named a member of the European Academy of Sciences and Arts (EASA). He will officially be inducted as a member at the organization's annual inauguration ceremony in early 2023.

Peter Markowich

Al-Khawarzmi Distinguished Professor, Applied Mathematics and Computational Science
A distinguished professor of applied mathematics and computational science, Dr. Markowich is the author of over 200 publications in leading international journals and circa 14,000 citations.

Julian David Gutierrez Pineda

Julian received both B.S. in Mathematics and B.S. in Engineering from Escuela Colombiana de Ingenieria, Colombia. After receiving his M.Sc. in Mathematical Sciences from Universidad Nacional Autónoma de México, México, he joined Professor Diogo Gomes as a Ph.D. student in applied mathematics at King Abdullah University of Science and Technology, Saudi Arabia. His research interests are primarily optimal control, optimal transport, calculus of variations, partial differential equations, and mean-field games.

Daria Bolbot

Short Bio Ms. Daria Bolbot is a Ph.D student in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. She graduated from Moscow Institute of Physics and Technology in 2013 with the Master degree in Applied Mathematics and Applied Physics. Ms. Daria got a Bachelor degree from the same institute (MIPT) 2 years earlier - 2011. Her research interests are Nonlinear Partial Differential Equations, Hyperbolic Conservation Laws, Dispersive-Diffusive Shock Waves and Dynamical
<> Computer Vision (12)

KAUST becomes first FIFA research institute in the Middle East and Asia

1 min read ·
KAUST has been designated the first FIFA Research Institute in the Middle East and Asia, and the fifth worldwide, reflecting the University’s commitment to advancing innovation in football through cutting-edge scientific inquiry.

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal Aldossary is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Abdelrahman Eldesokey

Postdoctoral Research Fellow
Abdelrahman Eldesokey is a Postdoctoral Fellow at KAUST, working on Generative AI and Computer Vision. His research focuses on diffusion models, multimodal large language models, and vision foundation models, aiming to make generative systems more controllable, interpretable, and reliable.
Najmeddine Dhieb

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Ganesh Sundaramoorthi

Ganesh Sundaramoorthi was an Assistant Professor of Electrical Engineering and jointly Assistant Professor of Applied Mathematics and Computational Science at KAUST starting in 2011. He is currently Principal Research Scientist at United Technologies Research Center in East Hartford, CT, USA, formulating computer vision algorithms for robotic inspection applications. Education and Early Career His PhD is in Electrical and Computer Engineering from the Georgia Institute of Technology in Atlanta, GA, USA in 2008. His PhD developed fundamental shape optimization methods for computer vision that
<> HPC (12)

Energy-Efficient and Sustainable Spatial Modeling Using GPU Computing

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B9 L2 R2325

HPC

This talk highlights recent advances in energy-efficient and sustainable spatial modeling using GPU computing. It focuses on mixed-precision algorithms and scalable spatial statistical modeling that significantly reduce computational cost and power consumption while preserving scientific accuracy.

KAUST researchers define the emerging field of high-performance statistical computing (HPSC)

4 min read ·
KAUST researchers outline a new discipline for statistical computing at supercomputing scale.

KAUST master’s student Osama Dabbousi awarded Rhodes Scholarship

1 min read ·
KAUST master’s student Osama Dabbousi earns the prestigious Rhodes Scholarship, reflecting KAUST’s growing role in developing national research talent.

Amal Alghamdi

Saverio Pasqualoni

M.S. Student, Computer Science
Saverio is a Master’s student pursuing graduate studies (M.S. and Ph.D.) in Computer Science at KAUST, working in the SANDS research group on high-performance computing and collective communications, with research interests that bridge systems-level computer science and large-scale simulation through a strong mathematical foundation.
ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.
KAUST-CEMSE-The-International-Research-Conference-on-Multi-Grid-and-Multi-Scale-Methods-in-Computational-Science

KAUST International Research Conference on Multi-Grid and Multi-Scale Methods in Computational Science, IMG 2025

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Building 19 and 20

The International Conference on Multigrid and Multiscale Methods in Computational Science (IMG) offers a dynamic forum for researchers to share advances in multigrid, multilevel and multiscale computational techniques, as well as their applications across scientific domains.

KAUST scientists win prestigious Gauss Award for supercomputing excellence

1 min read ·
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy, have won the German Gauss Center for Supercomputing (GCS) Award for original research that best advances high-performance computing. This makes KAUST the first Middle Eastern institution to receive this prestigious award.

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

ECRC contributions to SIAM CSE21

1 min read ·
ECRC @ SIAM CSE21! Follow us @KAUST_ECRC As in previous SIAM conferences on Parallel Processing (PP) and Computational Science and Engineering (CSE), ECRC members and their collaborators will have a strong presence at SIAM CSE21, nominally in Fort Worth, Texas, but held virtually due to the pandemic. ************************************************** SIAM CSE21 will run virtually with live sessions. ************************************************** Registration to SIAM CSE is required to gain access to the online platform and attend live sessions. Once registered, please use your personal

KAUST presents exascale expertise at SC21

1 min read ·
Supercomputing experts from KAUST converged with global participants at the America's Center in St. Louis, Missouri from November 14 to 19 for SC21, the annual international conference for high performance computing (HPC), networking, storage and analysis. Established in 1988, the SC conference is the largest HPC event of its kind, with big name industries like Intel, Red Hat and Oracle sharing the exhibition floor alongside universities, government agencies and small businesses, and professionals from various affiliations attending presentations throughout the convention center. In non

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.
<> visual computing (11)

Enabling Accurate and Efficient Simulation in Visual Computing - Public Colloquium

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KAUST

The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations. This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Visualization helps science to see the unexpected

1 min read ·
Advances in how science is presented means that visual tools can inspire research, as well as make its results accessible to the world.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Nanovisualization Research Group is hiring

1 min read ·
Nanovisualization Research Group is hiring scientific staff on the levels of Research Scientist, Postdoctoral Fellow, and Graduate Student. Follow the links to apply for the respective position: Research Scientist position Postdoctoral Fellow Graduate Student

Mastering a prickly problem in ferrofluids

1 min read ·
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Jorge Alejandro Amador Herrera

Research Interest His research interests include scientific and visual computing, numerical simulations, machine learning, and the modelling of natural phenomena. He works on physically-based simulations. Honors and Awards During his studies at UNAM, Alejandro has been awarded many times. In 2013, he received the UNAM Scholarship from the university. He was also awarded the Telmex Foundation Scholarship from 2013 to 2016, and the CONACYT Fellowship from 2017 to 2018. He graduated with summa cum laude in 2018. Alejandro has also received multiple awards from his participation in academic

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Dmitry Lyakhov

Dmitry Lyakhov is a Research Scientist at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), working with Professor Dominik Michels in the Computational Sciences Group (CSG). Education and Early Career Dmitry Lyakhov graduated from Belarusian State University with a master's degree in Theoretical Physics and, later on, a Ph.D. in Mathematics. He is also an alumnus of the National Academy of Sciences of Belarus and visiting scholar of von Karman Institute for Fluid Dynamics. In 2018, he received a Nanodegree in Machine Learning. Before joining KAUST
<> algorithm (10)

KAUST professor Peter Richtárik receives Charles Broyden Prize for second consecutive year

2 min read ·
The prize honors work advancing communication-efficient optimization for large-scale distributed learning systems.

Convergence of Descent Methods under Polyak- Lojasiewicz-Kurdyka Properties

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B1 L3 R3119

This talk presents a novel convergence analysis for a class of descent methods in nonsmooth and nonconvex optimization under the Polyak-Lojasiewicz-Kurdyka properties, proving finite termination under certain conditions and revealing incompatibilities between these properties and gradient Lipschitz continuity in certain frameworks.

Unveiling the potential of energy/entropy stable numerical methods for hyperbolic/mixed hyperbolic-parabolic PDEs

-

B9 L2 H1

Abstract The demand for increasingly multidisciplinary reliable simulations, for both analysis and design optimization purposes, requires transformational advances in individual components of future PDEs solvers. At the algorithmic level, hardware compatibility and efficiency are of paramount importance in determining viability on future hardware. However, equally important (if not more so) is provable algorithmic robustness which becomes progressively more challenging to achieve as problem size and physics complexity increase. We show that rigorously designed adaptive semi- and fully-discrete

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Robots learn by checking in on team members

1 min read ·
Innovative drone designs and software enables a team of drones to work together in a coordinated approach.

A race against time

1 min read ·
The theme of the University's 2019 Winter Enrichment Program (WEP) was "time." It represented an opportune moment for KAUST to welcome members of the McLaren Group to campus to discuss the recently signed extreme performance research partnership between KAUST and McLaren.

Changing how we predict coral bleaching

1 min read ·
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Teamwork pays off at startup Insyab

1 min read ·
Insyab, a technology startup specializing in smart solutions allowing robots and drones to collaborate on the execution of common tasks, resulted from three years of its founders' dedicated research at KAUST.

An algorithm for success: KAUST-designed differential equation-solving algorithm included in Maple 2020

1 min read ·
A KAUST-designed symbolic algorithm to solve nonlinear ordinary differential equations (ODEs) has been selected for inclusion in the current version of the computer algebra system (CAS) Maple 2020. The global symbolic and numeric computing software system—developed by the Canadian company Waterloo Maple (Maplesoft)—is widely used by scientists, engineers, and researchers to analyze, visualize, and solve mathematical problems.

Xun Qian

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected
<> electrical engineering (10)

A different kind of light

1 min read ·
The discovery of the incandescent light bulb has transformed human existence. New LED technologies promise to be the next step forward.

Better routing for wireless networks

1 min read ·
An improved wireless communication protocol enables more robust links between remote electronic devices.

Wireless devices tune-in to cloud power

1 min read ·
Next-generation mobile networks can use cloud computing algorithms to manage the increasingly high data demands of users.

Color-changing coatings ready for the big time

1 min read ·
The dazzling colors of peacock feathers arise from the physical interaction of light with biological nanostructures. Researchers have discovered how to exploit this natural trickery known as structural coloration into a large-scale printing technology that produces lightweight and ultraresistant coatings in any color desirable.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Lighting the way to the underwater Internet of things

1 min read ·
An optical system for monitoring underwater sensor positions could enable large networks of devices to be deployed for ocean measurements.

Tiny, fast, accurate technology on the radar

1 min read ·
A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Patterned optical chips keep secrets perfectly safe

1 min read ·
Chaos could help put cyberhackers out of business with a patterned silicon chip that will be uncrackable even in the future.

A clique away from more efficient networks

1 min read ·
An old branch of mathematics finds a fertile new field of application.

Unleashing the potential of tethered drones

1 min read ·
Wire-connected drones may complement or replace the fixed base stations of cellular communications networks.
<> Signal processing (10)

Racha Zayni

Visiting Researcher, Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Reem Arnaout

Visiting student at King Abdullah University of Science and Technology (KAUST).

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon
Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Alaa Alameer Ahmad

Research visit to communication theory lab in computer, electrical and mathematical sciences and engineering. KAUST (King Abdullah University of Science and Technology) ,Worked with research groups supervised by Prof. Mohamed-Slim Alouini and Prof. Tareq AL-Naffouri.

Abdullatif Al-Rabah

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).
<> cybersecurity (9) <> stochastic differential equations (9)

Shyam Mohan Subbiah Pillai

KAUST alumnus Dr. Abdul-Lateef Haji-Ali receives prestigious Humboldt Research Fellowship for Experienced Researchers

2 min read ·
KAUST alumnus Abdul-Lateef Haji-Ali receives Humboldt Fellowship for advances in stochastic modeling

Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

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B9 H1 R2322

KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation

Nikolas Kantas, Associate Professor, Department of Mathematics, Imperial College London

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KAUST

We consider the problem of parameter estimation for a McKean stochastic differential equation, and the associated system of weakly interacting particles. The problem is motivated by many applications in areas such as neuroscience, social sciences (opinion dynamics, cooperative behaviours), financial mathematics, statistical physics. We will first survey some model properties related to propagation of chaos and ergodicity and then move on to discuss the problem of parameter estimation both in offline and on-line settings. In the on-line case, we propose an online estimator, which evolves according to a continuous-time stochastic gradient descent algorithm on the asymptotic log-likelihood of the interacting particle system. The talk will present our convergence results and then show some numerical results for two examples, a linear mean field model and a stochastic opinion dynamics model. This is joint work with Louis Sharrock, Panos Parpas and Greg Pavliotis. Preprint: https://arxiv.org/abs/2106.13751

Recent advances in mixed finite element approximation for poroelasticity

Arbaz Khan, Assistant Professor, Department of Mathematics, Indian Institute of Technology (IIT)

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B1 L3 R3119

This talk discusses the non-conforming approximation of Biot's consolidation model. In the first part of the talk, we discuss posteriori error estimators for locking-free mixed finite element approximation of Biot’s consolidation model. In the second part of the talk, we discuss a novel locking-free stochastic Galerkin mixed finite element method for the Biot consolidation model with uncertain Young’s modulus and hydraulic conductivity field.

International Agenda for KAUST's SRI-UQ

1 min read ·
SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Conference on Advances in Uncertainty Quantification Methods, Algorithm and Applications (#UQAW2016)

1 min read ·
The 4th consecutive year that KAUST is holding the UQAW annual event dedicated to reporting on the state-of-the-art and latest advances in the methods used to quantify the calculation uncertainties. The aim of the discussion forum in exchanging information on innovative algorithm design as well as their practical applications and performances in experimental settings.

Raul Tempone

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Erik von Schwerin

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.
<> communications (8)

On the resolution of a theoretical question related to the nature of local training in federated learning

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B1 L3 R3119

In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.

Prof. Alouini selected as an IEEE Communications Society Distinguished Lecturer for 2016 and 2017

1 min read ·
Congratulations to Prof. Alouini for being selected to give a Distinguished Lecture for 2016 and 2017 in IEEE Communications Society

Marie Sklodowska-Curie Fellowships in Photonics @ CEMSE – call 4 - 2018

1 min read ·
A group of professors working in the area of photonics at KAUST, most of them with CEMSE Division, offer hosting of outgoing Marie Sklodowska-Curie Fellowships in their research groups within the EU H2020 COFUND Postdoctoral Fellowship Programme MULTIPLY.

Lighting the way to the underwater Internet of things

1 min read ·
An optical system for monitoring underwater sensor positions could enable large networks of devices to be deployed for ocean measurements.

Tiny, fast, accurate technology on the radar

1 min read ·
A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.

Patterned optical chips keep secrets perfectly safe

1 min read ·
Chaos could help put cyberhackers out of business with a patterned silicon chip that will be uncrackable even in the future.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Mohamed-Slim Alouini

Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering
KAUST is a fertile place for any researcher who wants to develop rigorous computational and engineering methods to solve real-world problems. Its unique support of science allows the most advanced scientific revolution.
<> computing (8)

The two faces of correlation

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KAUST

Discussing the concept of correlation and how to interpret it alone (marginally) or within a more complex environment (conditionally). This rather simple observation is the key observation behind a lot of exciting developments and connections in statistics that can be leveraged for improved computations and better motivated statistical models.

Taking the guesswork out of experimental design

1 min read ·
A fast computational method optimizes sensor measurement networks for noisy, sparsely observed environments.

Taking graphics cards beyond gaming

1 min read ·
A highly efficient mathematical solver designed to run on graphics processors gives scientists and engineers a powerful new tool for a common computational problem.

Giving power to demand-driven electricity

1 min read ·
A new approach to analyzing household electricity: smart-meter data could improve the performance and efficiency of national power grids.

Improving connections for spatial analysis

1 min read ·
A statistical model that accounts for common dependencies in spatial data yields more realistic results for studies of temperature, wind and pollution levels.

One bad apple can spoil a robot swarm

1 min read ·
Coordinated robot swarms can achieve amazing feats, but even a single faulty unit can have serious consequences.

Zooming in on brain function

1 min read ·
Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Point patterns help to predict landslides

1 min read ·
Rainfall simulations in statistical models could allow accurate prediction of dangerous landslides.
<> data science (8)

KAUST Research Conference on Mathematical and Data Sciences

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KAUST Campus

Theory and applications of data science.

Proper Random Walk Spline Models

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B3 L5 R5209

This dissertation introduces the Proper Random Walk of order 2 (PRW2), a full-rank Gaussian Markov random field that provides a principled alternative to intrinsic random walk (RW2) priors. By construction, RW2 models exhibit heteroscedastic marginal variances, inflated boundary effects, sensitivity to grid design, and unbounded forecast uncertainty—features that undermine the reliability of inference, particularly in sparse-data settings or beyond the observed domain.

2018 Workshop on Statistics and Data Science

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Auditorium 0215 between Buildings 4 & 5

The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

Lifting up female scientists

1 min read ·
Women in Data Science (WiDS) is like the Global Women's March for analytics; it's a phenomenon too big for just one city. WiDS is the largest data science conference on Earth—taking place in over 50 countries, it has attained more than 100,000 attendees and is tagged as a global movement. The annual conference aims to inspire and educate data scientists worldwide—regardless of gender—and support women in the field. This year's WiDS conference was held at Stanford University with more than 100 regional institutions all over the world participating, including KAUST.

Joining forces to advance healthcare analytics in the Kingdom

1 min read ·
The recent KAUST-Prince Mohammed Bin Salman College (MBSC) Invitational Healthcare Analytics and Data Science Workshop brought together top-level clinicians, healthcare executives, representatives to discuss digitization and advancement of healthcare analytics in Saudi Arabia.

KAUST STAT students come together for second ASA chapter meeting

1 min read ·
Members of the KAUST American Statistical Association (ASA) student chapter recently came together for the group’s second online meeting held on Tuesday, November 10, 2020. The meeting served as an orientation exercise for new KAUST Statistics (STAT) Program students while also highlighting the shared experience of STAT Ph.D. candidates: Jian Cao, Wanfang Chen, Yuxiao Li, and Gaurav Agarwal.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.

Eman Kabbas

Deeply passionate about Bayesian statistics, Eman Kabbas is a Math/Statistics Lecturer in the General Studies Department at Jubail Industrial College (JIC). Currently, Eman is a Ph.D. candidate in Applied Mathematics and Computational Sciences at KAUST, specializing in Bayesian statistics under the supervision of Professor Håvard Rue.
<> mathematics (8)

Houcem Ben Salem

Intern (former), Information Science Lab
Research Intern at KAUST(King Abdullah University of Science and Technology) .

Applied Mathematics School 2024

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The KAUST Applied Mathematics School is a 1-week visiting program aiming to introduce undergraduate and master's students to special research topics focusing on key areas in applied mathematics and computational science.

The Discrete Charm of Geometry

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B9 L2 H2

How is modern mathematical teamwork carried out? The multiple award-winning film The Discrete Charm of Geometry by Ekaterina Eremenko will screen on April 3rd after the CEMSE Dean's Distinguished Lecture Discrete conformal mappings and Riemann Surfaces: Theory and Applications by Prof. Alexander I. Bobenko, Technische Universität Berlin. Following the screening, Prof. Bobenko will be available for a Q&A session.

Discrete conformal mappings and Riemann Surfaces: Theory and Applications

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B9 L2 H2

The general idea of discrete differential geometry is to find and investigate discrete models that exhibit properties and structures characterisitic of the corresponding smooth geometric objects. This is a challenging problem, since equivalent points of view in the smooth setting may lead to a number of inequivalent treatments in the discrete setting. We will illustrate the paradigm of structure-preserving discretizations on the example of conformal maps by showing how simple definitions lead to surprisingly rich theories.

Efficiency Assessment of Approximated Spatial Predictions for Large Datasets

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KAUST

Our suggested criteria are more useful for the determination of tuning parameters for sophisticated approximation methods of spatial model fitting. To illustrate this, we investigate the trade-off between the execution time, estimation accuracy, and prediction efficiency for the TLR method with intensive simulation studies and suggest proper settings of the TLR tuning parameters.

Mathematician Peter Markowich named 2022 AMS Fellow

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich joins 45 mathematical scientists from around the world named 2022 Fellows of the American Mathematical Society (AMS) program.
Sabine El Khoury

Sabine El Khoury

Sabine El Khoury is an instructional professor of applied mathematics and computational sciences specializing in commutative algebra.

Florian Rist

Florian Rist is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at KAUST. He is a founding member of the Center for Geometry and Computational Design at TU Vienna and currently holds a double affiliation as a research scientist at the Visual Computer Center at the King Abdullah University of Science and Technology and as a senior scientist at the Institute of Arts and Design at TU Vienna. He studied at TU Munich and the University of Technology Tokyo and is a licensed architect and member of the Chamber of Architects in Bavaria, Germany. Before
<> photonics (8)

Photonic topological phases in dispersive metamaterials

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B1 L4 R4214

We analyze the photonic topological phases in dispersive metamaterials which satisfy the degenerate condition at a reference frequency, where the hybrid modes are decoupled and determined by two subsystems with degenerate eigenvalues. By introducing the pseudospin states as the eigenfield basis, the Hamiltonians of the hybrid modes represent the pesudospin-orbit interaction with spin 1, which result in nonzero spin Chern numbers that characterize the topological phases.

Quantum Inspired Photonics

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B1 L2 R4214

Quantum mechanics and photonics share mathematical equivalence. By carefully exploiting the interplay between optical index, gain and loss in the complex dielectric permittivity plane, optics has become an ideal platform to explore some exotic quantum concepts, such as topological physics and parity-time (PT) symmetry. Instead of counteracting optical losses at micro and nano scales in integrated photonics, we started from an opposite viewpoint and developed a new paradigm of positively and strategically manipulating optical losses by the quantum-inspired photonics concept enrich fundamental optical physics and realized novel photonic synthetic matters with unique optical functionalities. In this seminar, I will present our recent efforts on engineering the complex optical potentials at an exceptional point (i.e. PT symmetry transition point). Based on the exceptional point-induced unidirectionality, we harness optical losses to enable unique microlaser functionalities, in particular, an orbital angular momentum (OAM) microlaser that structures and twists the lasing radiation at the microscale, which is expected to address the growing demand for information capacity. Additionally, I will discuss non-Hermitian topological photonics where optical non-Hermiticity and topological physics are coupled.

Prof. Andrea Fratalocchi receives appreciation from Nature Publications as one of its best reviewers

1 min read ·
Prof. Andrea Fratalocchi received a letter of appreciation from the Editor-in-Chief of Nature for serving as a reviewer.

Klemens Katterbauer (PhD in Electrical Engineering) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper contest

1 min read ·
Klemens Katterbauer (Ph.D. candidate under supervision Andrea Fratalocchi) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper Contest.

Cancer screening advance: single point genetic mutations made visible by new optical chip

1 min read ·
The collaboration of the teams of professors Enzo Di Fabrizio and Andrea Fratalocchi at the King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, has led to the development of a new device that enables the detection of mutations down to a single amino acid.

Andrea Fratalocchi Elected OSA Fellow for Pioneering Innovations

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently elected as a Fellow Member of The Optical Society of America (OSA) at the Society’s Board of Directors meeting in September.

KAUST Associate Professor Andrea Fratalocchi elected Fellow of the Optical Society

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Science and Engineering division, was recently elected as a Fellow of the Optical Society (OSA) at the society's Board of Directors meeting in September.

Andrea Fratalocchi

Professor Fratalocchi is a pioneer in complex systems and sustainable technologies, focused on addressing critical global challenges. His groundbreaking research has led to innovative solutions in areas such as energy harvesting, clean water production and smart materials. As a founder of the ECE program at KAUST and co-founder of Pixeltra, Professor Fratalocchi is a proven leader and entrepreneur, successfully translating his research into real-world impact.
<> Red Sea (8)

Winds and waves winding down

1 min read ·
Wind intensities and wave heights in the Red Sea are decreasing over time—potential for a significant impact on its ecosystem.

More rain for the Red Sea if El Niño breezes in

1 min read ·
Modeling leads to a better understanding of the role El Niño plays in increasing rainfall along the coast of the Red Sea.

Microbial communities have a seasonal shake-up

1 min read ·
Turbulence and nutrient availability drive changes in Red Sea microbes.

Red Sea flushes faster from far-flung volcanoes

1 min read ·
Volcanic eruptions in Mexico and the Philippines can lead to atmospheric changes that favor the ventilation of deep water in the Red Sea.

Eddy currents affect flux of salt more than heat

1 min read ·
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.

Simulating Red Sea water exchanges

1 min read ·
Powerful computer simulations are revealing new insights into water exchanges between the Red Sea and the Gulf of Aden.

Remote sensing of toxic algal blooms

1 min read ·
Algal blooms in the Red Sea can be detected with a new method that accounts for dust storms and aerosols.

Predicting future hotspots in the Red Sea

1 min read ·
Predicting areas of the Red Sea most susceptible to overheating by the end of the century could help conservation efforts to protect its precious coral reefs.
<> spatio-temporal statistics (8)

Functional data depth and its application in the visualization of spatio-temporal covariance structures

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KAUST

Functional data analysis is a very active research area due to the overwhelming existence of functional data. In the first part of this talk, I will introduce how functional data depth is used to carry out exploratory data analysis and explain recently-developed depth techniques. In the second part, I will discuss spatio-temporal statistical modeling. It is challenging to build realistic space-time models and assess the validity of the model, especially when datasets are large. I will present a set of visualization tools we developed using functional data analysis techniques for visualizing covariance structures of univariate and multivariate spatio-temporal processes. I will illustrate the performance of the proposed methods in the exploratory data analysis of spatio-temporal data.

CEMSE Welcomes new Professor: Dr. Ying Sun

1 min read ·
Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.

The Lambert Award for Young Statisticians Goes To Raphael Huser

1 min read ·
Today Raphael Huser, Assistant Professor at King Abdullah University of Science and Technology (KAUST), received the Lambert Award for Young Statisticians for his contribution in modeling the distribution of extreme weather events.

Professor Sun receives the Environment Early Investigator Award 2017 for her outstanding contribution in environmental statistics

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

KAUST Assistant Professor Raphaël Huser receives American Statistical Association award

1 min read ·
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Using Statistical Models to Solve Environmental Problems

1 min read ·
Joining the faculty of King Abdullah University of Science and Technology (KAUST) six years ago gave statistics Professor Ying Sun a unique career opportunity: the chance to build and lead her own research group solving real-world problems.

Ying Sun

Professor, Statistics
Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.
<> bioscience (7)

Discovery of Low-Dimensional Generative Models for Complex Dynamical Systems

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This thesis presents a data-driven framework for discovering low-dimensional generative models of complex systems by using a library of normal-form equations to identify both observable dynamics and hidden control variables directly from time-series data.

Juan Pablo Muñoz Díaz

Ali O Balubaid

Ph.D Directed Research at KAUST (King Abdullah University of Science and Technology)

Assessing seawater safety

1 min read ·
Elevated bacterial populations in waters near beaches and urban areas could pose a problem for desalination plants.

Brain waves in boxes

1 min read ·
A statistical method helps to identify abnormal signals in electroencephalograms and locate their source in the brain.

Epilepsy study shows the shape of things to come

1 min read ·
Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

CovMT: Tracking virus mutations across the world

1 min read ·
An interactive platform helps users visualize where SARS-CoV-2 mutations start, how wide they spread and how infectious they are.
<> computational fluid dynamics (7)
KAUST-CEMSE-The-International-Research-Conference-on-Multi-Grid-and-Multi-Scale-Methods-in-Computational-Science

KAUST International Research Conference on Multi-Grid and Multi-Scale Methods in Computational Science, IMG 2025

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Building 19 and 20

The International Conference on Multigrid and Multiscale Methods in Computational Science (IMG) offers a dynamic forum for researchers to share advances in multigrid, multilevel and multiscale computational techniques, as well as their applications across scientific domains.

Luca Galimberti

Luca is a Ph.D. candidate in Applied Mathematics at KAUST, he focuses on the development of numerical methods for aeroacoustics and aerodynamics of aeronautical applications.

Space-time conservation element and solution element (CESE) method for computational fluid dynamics

Hua Shen

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B9 L2 H1

Space-time conservation element and solution element (CESE) method is a unique finite-volume-type method for computational fluid dynamics (CFD). This approach has several attractive properties, including: (i) unified treatment of the space and time such that only one step is required to construct high-order schemes; (ii) a highly compact stencil regardless of the order of the accuracy; (iii) easiness of extension to any arbitrary shape of polygonal elements. Since its inception, the CESE method has achieved great success in different areas.

On supercomputers and engineering applications: DNS, modeling, and GPUs

Mathis Bode, Researcher, Institute for Combustion Technology (ITV) at RWTH Aachen University

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B2 L5 R5209

The numerical solution of multi-physics problems relying on the Navier-Stokes equations has kept multiple generations of supercomputers busy. For fundamental problems, computational fluid dynamic aims to resolve all relevant time and length scales, which is then known as direct numerical simulation (DNS).

Rasha Aljahdali

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

Radouan Boukharfane

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Radouan Boukharfane joined Extreme Computing Research Center (ECRC) at King Abdullah University of Science & Technology (KAUST) as a postdoctoral fellow in 2019. He is working with Professor Matteo Parsani his research group. Prior to this, he spent a year as a Postdoctoral Research Fellow in the Department of Aerodynamics, Energetics and Propulsion (DAEP) of the National Higher French Institute of Aeronautics and Space (ISAE-SUPAERO) in Toulouse (France). Education and Early Career Dr. Boukharfane received a joint MSc. (2014) from Department of mechanical engineering at Ecole Polytechnique of

Gabriel Wittum

Dr. Gabriel Wittum, professor of applied mathematics and computational science at KAUST, is an expert in modelling and simulating problems in empirical science.
<> control systems (7)

Michał Drewniak

Michał is pursuing a collaborative Ph.D. carried out jointly by the King Abdullah University of Science and Technology (KAUST) and AGH University of Krakow (AGH). At KAUST, he is a Ph.D. student in Electrical and Computer Engineering working in the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou, while at AGH University he conducts his doctoral studies under the supervision of Professor Marcin Baszyński. Michał obtained both his Bachelor of Engineering and Master of Engineering degrees in Electrical Engineering from AGH

Professor Meriem Laleg's research on modeling contaminant transport published in SIAM Journal on Scientific Computing

1 min read ·
A new algorithm is proposed to estimate the average velocity, dispersion coefficient, and differentiation order of a space-fractional advection-dispersion equation used for modeling contaminant transport in porous media. This allows for the characterization of the medium and the determination of the contaminant source. The algorithm is efficient, robust and fast.

Professor Meriem Laleg's research on brain response models published in Journal of Neuroscience Methods

1 min read ·
A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements.

Professor Meriem Laleg's research on membrane distillation modeling for desalination published in Desalination

1 min read ·
An accurate mathematical model is proposed to describe an emerging desalination technology called direct contact membrane distillation system. The mathematical model is important for designing efficient control and monitoring strategies, a crucial step to facilitate the commercialization of this technology.

Team KAUST places 3rd at the Mohamed Bin Zayed International Robotics Challenge

1 min read ·
A team from the King Abdullah University of Science and Technology (KAUST) competed in the inaugural Mohamed Bin Zayed International Robotics Challenge (MBZIRC), held in Abu Dhabi, UAE, March 2017, and took a 3rd place finish in the ground robotics challenge.

Faculty Focus: Taous-Meriem Laleg-Kirati

1 min read ·
-By A. Ortega, KAUST News KAUST Associate Professor of Electrical Engineering Taous-Meriem Laleg-Kirati joined the University in 2011 after working as a researcher at INRIA in France. Laleg-Kirati leads the Estimation, Modeling and ANalysis (EMAN) research group at KAUST, and her interests lie in control theory, system modeling and signal applications. She investigates multiple engineering disciplines and develops mathematical models and algorithms that control processes relying on real-time feedback. "My research goals [are the development of] effective estimation methods and algorithms to

Bacem Ben Nasser

Bacem Ben Nasser is a postdoctoral researcher fellow in the Electrical Engineering department within the CEMSE division. He obtained his Ph.D. degree in Sciences and Technology-Mathematics from the University of Sfax, Faculty of Sciences of Sfax, Tunisia, where he worked in Lyapunov and control theories, nonlinear integral inequalities and time scale calculus. In January 2017, he was affiliated with the University of Kairouan, Tunisia, at the Higher Institute of Applied Sciences and Technology of Kairouan as assistant professor of applied mathematics. Dr. Ben Nasser joined the research group
<> Dynamical Systems (7)

Computing Heteroclinic Orbits for Dynamical Systems

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B1 L3 R3119

This course provides an overview of the most well known methods for computing heteroclinic orbits for dynamical systems.

AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory

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B9 L3 R3120

This talk describes a novel machine learning algorithm, inspired by complex analysis and dynamical systems theory, that significantly improves efficiency in solving partial differential equations and scientific computing, while providing a theoretical framework for reconstructing complex, unknown systems from partial observational data.

Discovery of Low-Dimensional Generative Models for Complex Dynamical Systems

-
This thesis presents a data-driven framework for discovering low-dimensional generative models of complex systems by using a library of normal-form equations to identify both observable dynamics and hidden control variables directly from time-series data.

Juan Pablo Muñoz Díaz

Majed Sofiani

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Jan Haskovec

Jan Haskovec

Stochastic, partial and functional differential equations applied to study of dynamical processes in physics, biology and social sciences. Optimal transportation networks. Consensus formation with applications to engineering (swarm robotics).

Daria Bolbot

Short Bio Ms. Daria Bolbot is a Ph.D student in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. She graduated from Moscow Institute of Physics and Technology in 2013 with the Master degree in Applied Mathematics and Applied Physics. Ms. Daria got a Bachelor degree from the same institute (MIPT) 2 years earlier - 2011. Her research interests are Nonlinear Partial Differential Equations, Hyperbolic Conservation Laws, Dispersive-Diffusive Shock Waves and Dynamical
<> energy harvesting (7)

Prof. Andrea Fratalocchi receives appreciation from Nature Publications as one of its best reviewers

1 min read ·
Prof. Andrea Fratalocchi received a letter of appreciation from the Editor-in-Chief of Nature for serving as a reviewer.

Klemens Katterbauer (PhD in Electrical Engineering) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper contest

1 min read ·
Klemens Katterbauer (Ph.D. candidate under supervision Andrea Fratalocchi) comes 2nd place at the Middle East, North Africa & South Asia SPE Student Paper Contest.

Cancer screening advance: single point genetic mutations made visible by new optical chip

1 min read ·
The collaboration of the teams of professors Enzo Di Fabrizio and Andrea Fratalocchi at the King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, has led to the development of a new device that enables the detection of mutations down to a single amino acid.

A breath of fresh information to help diagnosis

1 min read ·
A communications concept could pinpoint a person infected with a deadly, contagious virus in the middle of a crowded airport.

Andrea Fratalocchi Elected OSA Fellow for Pioneering Innovations

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently elected as a Fellow Member of The Optical Society of America (OSA) at the Society’s Board of Directors meeting in September.

KAUST Associate Professor Andrea Fratalocchi elected Fellow of the Optical Society

1 min read ·
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Science and Engineering division, was recently elected as a Fellow of the Optical Society (OSA) at the society's Board of Directors meeting in September.

Andrea Fratalocchi

Professor Fratalocchi is a pioneer in complex systems and sustainable technologies, focused on addressing critical global challenges. His groundbreaking research has led to innovative solutions in areas such as energy harvesting, clean water production and smart materials. As a founder of the ECE program at KAUST and co-founder of Pixeltra, Professor Fratalocchi is a proven leader and entrepreneur, successfully translating his research into real-world impact.
<> fluid dynamics (7)
Analysis and PDEs Workshop-2025

Workshop on Analysis and PDEs

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B3, L5, R5220

Join us for the Workshop on PDEs to explore advances, challenges and methods in the analysis and computation of partial differential equations.

Katerina Nik

Professor Nik studies partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering.

Particle Approximations For Partially Observed Fluid Dynamics Models

Professor Dan Crisan, Mathematics, Imperial College London

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KAUST

Fluid dynamics models are ubiquitous in a multitude of applications. One of the most important applications of fluid dynamics models is numerical weather prediction. Modern numerical weather prediction combines sophisticated nonlinear fluid dynamics models with increasingly accurate high-dimensional data. This process is called data assimilation and it is performed every day at all major operational weather centers across the world. Data assimilation (DA) requires massive computing capabilities as realistic atmosphere-ocean models typically have billions of degrees of freedom. I will give a short overview of the ongoing research that aims to drastically decrease the required DA computational effort by reducing the dimension of the models involved and using stochastic perturbations to account for the unresolved scales. The incorporation of observation data is done by using particle approximations suitably adapted to solve high-dimensional problems.

Better racing car design through an industry partnership

1 min read ·
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.
KAUST-CEMSE-Matteo-Parsani

Matteo Parsani

Professor Parsani develops robust, variable order and self-adaptive algorithms for solving fluid flow problems in complex geometries. The application domains currently driving Parsani's research are compressible computational aerodynamics, computational aeroacoustics for noise reduction in vehicles and molecular communication.

Irving Enrique Reyna Nolasco

Research Interests Irving Enrique Reyna Nolasco's research interests include numerical methods for partial differential equations, large eddy simulations, high order entropy stable schemes, machine learning, complex systems, and non-linear dynamics. Why did you choose your field of research? As a graduate student, I have always found satisfaction in developing scientific software and libraries for the simulation and visualization of different phenomena. I enjoy applying scientific computing tools and mathematical concepts to relevant problems in physics. By the end of my bachelor studies, I

Daniele Boffi

Professor Boffi is a leading international expert in mixed finite elements and the approximation of eigenvalue problems arising from partial differential equations.
<> free boundary problems (7)

The invisible architect

4 min read ·
Most people never see the mathematics running beneath modern life. CEMSE Professor Miguel Urbano is making sure it works when it matters most.

First-Order Mean-Field Games with Entry-Exit Flow Constraints and Contact-Set Conditions

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This dissertation develops and rigorously analyzes first-order mean-field game models incorporating novel mixed boundary conditions to realistically represent population dynamics in bounded domains by eliminating unrealistic entry phenomena that are typically induced by standard Dirichlet conditions.
Analysis and PDEs Workshop-2025

Workshop on Analysis and PDEs

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B3, L5, R5220

Join us for the Workshop on PDEs to explore advances, challenges and methods in the analysis and computation of partial differential equations.

Katerina Nik

Professor Nik studies partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering.

A brief introduction to the obstacle problem regularity theory

Prof. Eduardo Teixeira, Department of Mathematics, University of Central Florida (USA)

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B1 L4 R4102

Abstract How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary. Brief Biography Eduardo Teixeira is a Full

Rafayel Teymurazyan

Rafayel Teymurazyan works on nonlinear partial differential equations and free boundary problems arising from applications in bio-technologies, chemical engineering, financial mathematics, and optimal design, among others.

Miguel Urbano

Professor Urbano’s research interests are in regularity theory for nonlinear partial differential equations, in particular of singular or degenerate type, arising from different applications, like phase transitions, flows in porous media or semi-supervised learning. He is also interested in free boundary problems, focusing on understanding the local behaviour of weak solutions and the geometric properties of interfaces.
<> human health (7)

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

Deep learning in the emergency department

1 min read ·
Harnessing the power of deep learning leads to better predictions of patient admissions and flow in emergency departments.

CovMT: Tracking virus mutations across the world

1 min read ·
An interactive platform helps users visualize where SARS-CoV-2 mutations start, how wide they spread and how infectious they are.

Finding true protein hotspots in cancer research

1 min read ·
Novel statistical methods help tell the difference between "false positives" and true detections of protein domain hotspots that could be linked to cancer.

Can machine learning help predict disease spread?

1 min read ·
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.
<> IoT (7)
Salem Alsaqaf

Salem Al-saqaf

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri
Ruochen Zhu

Ruochen Zhu

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).
<> mathematical modelling (7)

On the Modeling and Approximation of Phase Transitions in Elasticity

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B9 L2 R2325

This talk explores the mathematical modeling of phase transitions in elasticity, drawing motivation from observed phenomena in crystalline solids and biomaterials.
kaust-mathematical-modeling-reaction-diffusion-biomedical-research

Bringing an old proof to modern problems

1 min read ·
A physically consistent and self-adaptive numerical modelling approach opens the door to more accurate and reliable computational simulations of clinical and biological processes.

Professor Marc Genton named Distinguished Professor of Statistics at KAUST

1 min read ·
Marc G.Genton, Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), has been appointed by KAUST President Jean-Lou Chameau to Distinguished Professor of Statistics.

Stefanos Georgiadis

Stefanos Georgiadis is a Ph.D. candidate in a co-tutelle between KAUST and TU Wien, under the supervision of Professor Athanasios Tzavaras and Ansgar Jüngel. Before joining KAUST, Stefanos obtained his bachelor and master's degree in applied mathematics from the School of Applied Mathematical and Physical Sciences of the National Technical University of Athens in Greece. Research Interests Stefanos' research interests include the analysis of nonlinear partial differential equations as they apply to the modeling of multicomponent systems of gases.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.

Dmitry Lyakhov

Dmitry Lyakhov is a Research Scientist at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), working with Professor Dominik Michels in the Computational Sciences Group (CSG). Education and Early Career Dmitry Lyakhov graduated from Belarusian State University with a master's degree in Theoretical Physics and, later on, a Ph.D. in Mathematics. He is also an alumnus of the National Academy of Sciences of Belarus and visiting scholar of von Karman Institute for Fluid Dynamics. In 2018, he received a Nanodegree in Machine Learning. Before joining KAUST

Athanasios Tzavaras

KAUST Professor of Applied Mathematics and Computational Science Athanasios (Thanos) Tzavaras specializes in mathematical modelling, analysis and computation of partial differential equations pertaining to fluid mechanics or modeling of materials.
<> neuroscience (7)

Simulation of Neuronal Signal Processing - 2020-03-05

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B9 L2 R2322

In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.

Connecting the dots to unlock brain structure

1 min read ·
Mapping genetic influences on connections between neural networks could lead to a better understanding of brain organization and behavior.

Stimulating deeper insights into brain function

1 min read ·
Modeling changes in brain activity over time provides deeper insights into learning and behavioral responses.

Making collective sense of brainwaves

1 min read ·
A new statistical tool for collectively analyzing large sets of brainwaves promises to accelerate neurofunctional research.

A brainwave from social networks

1 min read ·
The statistics used to understand social networks reveal the diversity of functional connections in the brain.

Extreme analysis of seizures

1 min read ·
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.

Gabriel Wittum

Dr. Gabriel Wittum, professor of applied mathematics and computational science at KAUST, is an expert in modelling and simulating problems in empirical science.
<> Photonics and optoelectronics (7)
polina.morozova photo

Polina Morozova

M.S. Student, Photonics Laboratory
md alshahriar shakil photo

Md Al Shahriar Shakil

M.S. Student, Photonics Laboratory
Md Al Shahriar Shakil was awarded his Bachelor of Science degree in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology (BUET), where his undergraduate research focused on quantum computing and quantum photonics. After completing his bachelor’s degree, he worked as a Research Assistant at BUET, where he gained experience in spin-based quantum qubits for quantum computing. He is currently an MS/PhD student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), where his research focuses on
Hanqi Li profile picture

Hanqi Li

M.S. Student, Photonics Laboratory
Hanqi Li received her Bachelor of Science degree in Electronic Science and Technology from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2025. She was an intern student at the Institute of Semiconductors, Chinese Academy of Sciences, during 2024 and 2025, where she worked on topics related to optoelectronics and silicon photonics. Hanqi is currently an MS/PhD student in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, and a member of the Photonics Laboratory.
Mohammed Alotaibi

Mohammed Obidallah Alotaibi

Ph.D. Student, Photonics Laboratory
I am Mohammed Obidallah Alotaibi, joining the KAUST Photonics Lab. I hold an M.S. in Electrical Engineering from the University of Central Florida and a B.S. from Umm Al-Qura University. Previously, I served as an Instrument Engineer at Aramco and currently lecture at Taibah University. I am passionate about advancing photonic technologies in energy and sustainability applications, and I look forward to contributing to the lab’s research in optoelectronics and integrated photonic systems. Previous Work Experience: Instrument Engineer, Saudi Aramco (2010–2012) Teaching Assistant & Lecturer

Yan Wang

Ph.D. Student, Photonics Laboratory
M.S./Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Aug. 2024- present BEng. in Communication Engineering at University of Electronic Science and Technology of China (UESTC), Chengdu, China, Sept. 2019 - Jun. 2024​
Jiahoa Wei profile picture

Jiahao Wei

M.S. Student, Photonics Laboratory
Education Jan 2025 - present, M.S./Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Sep 2020 - Jun 2024, B.S. in Optoelectronic Information Science and Engineering at Southern University of Science and Technology (SUSTech), Shenzhen, China.
Zinan Xiao profile picture

Zinan Xiao

Ph.D. Student, Photonics Laboratory
Education Aug 2024 - present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Oct 2020 - Oct 2021, M.S. in Optics and photonics at Imperial College London, London, UK. Sep 2016 - Jun 2020, B.S. in Optoelectronic information engineering at Chongqing University, Chongqing, China.
<> 6G (6)

Nebras Filimban

Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

6G: The time is now!

1 min read ·
While 5G companies and opponents are competing over the deployment of the first 5G platforms, the next generation of wireless telecommunication technology is being concocted right now in laboratories all around the world.

New research aims to bridge the digital divide

1 min read ·
By the end of 2018, there were nearly 4 billion people without Internet connectivity in the world. Almost 75% of this unconnected population is located in 20 countries and is concentrated in rural, low-income, and low-literacy areas.

Connecting the Unconnected at the KAUST 6G Summit

1 min read ·
The inaugural 6G Summit on "Connecting the Unconnected" took place on Tuesday, October 27, and featured a host of international researchers and experts in the areas of Internet and Communication Technology (ICT) and Wireless Communications System (WCS).

Shaping a 6G future

1 min read ·
This coming decade promises to be yet another revolutionary one in how we communicate. In a world that is only beginning to embrace 5G technology, several scientific institutions, governments, telecom companies, and phone manufacturers worldwide are beginning to research the development possibilities of its successor—6G technology.
<> Bayesian computational statistics (6)

Haziq Jamil

Research Specialist, Statistics
Haziq Jamil is a Research Specialist at the King Abdullah University of Science and Technology. His research focuses on statistical theory, methods and computation, with a special inclination towards social science applications.

Exploring Non-Linear Interactions in Multivariate Time Series

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KAUST

Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.

Bayesian Deep Learning

Maurizio Filippone, Associate Professor, EURECOM, France

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B9 L3 R3128

The impressive success of Deep Learning (DL) in predictive performance tasks has fueled the hopes that this can help addressing societal challenges by supporting sound decision making. However, many open questions remain about their suitability to hold up to this promise. In this talk, I will discuss some of the current limitations of DL, which directly affect their wide adoption. I will focus in particular on the poor ability of DL models to quantify uncertainty in predictions, and I will present Bayesian DL as an attractive approach combining the flexibility of DL with probabilistic reasoning. I will then discuss the challenges associated with carrying out inference and specifying sensible priors for DL models. After presenting a few of my contributions to address these problems, I will conclude by presenting some interesting emerging research trends and open problems which define my current research agenda.

Solving real-world problems

1 min read ·
A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

Miguel Angel Alvarez Ballesteros

Miguel Angel Alvarez Ballesteros is a M.S./Ph.D. candidate in the KAUSTStochastic Numerics research group under the supervision of Professor Raul Tempone. Before joining KAUST, Miguel obtained a bachelor's degree in physics from the Universidad Industrial de Santander, Bucaramanga, Colombia. Research Interests Miguel's research interests include Monte Carlo methods, Bayesian Statistics, Particle filtering and Machine learning. Education Profile Bachelor's degree in Physics from the Universidad Industrial de Santander (UIS), Bucaramanga, Colombia, in 2019. Publications Álvarez, M., Orjuela

Haavard Rue

Program Chair, Statistics
Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.
<> Computational mathematics (6)

Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective

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B1 L3 R3119

This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

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B9, L2, R2322

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

Theodoros Katsaounis

Dr. Theodoros research interests are in the area of applied and computational mathematics with emphasis on the development, analysis and implementation of numerical methods for approximating solutions of PDE’s modelling various physical processes.

Pierre-David Letourneau

Visiting Student, Waves in Complex Media
Visiting Student, April 2012 - June 2012 Introduction ​Pierre-David Letourneau is currently a PhD candidate at Stanford University. He visited us from April 2012 to June 2012 and worked on fast algorithms for strong-scattering large-scale systems, and imaging through clutter media. Research Interests Pierre-David's research interests lie in the area of applied and computational mathematics with topics including imaging, inverse problems, waves in heterogeneous media as well as fast algorithms. His work focuses on both the theoretical and computational aspects of wave propagation, and includes

Jongho Park

Jongho Park is a Research Scientist at King Abdullah University of Science and Technology (KAUST) where his research focuses on the design and analysis of efficient numerical methods for variational problems.

Ioanna Mousikou

Short Bio Ms. Ioanna Mousikou is a Ph.D student in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. During her second year at KAUST, she received a Master's degree in Applied Mathematics where the thesis was focused on numerical methods for hyperbolic conservation laws. Before joining KAUST, Ms. Ioanna completed undergraduate degree in Mathematics at the University of Cyprus. Education Profile M.Sc. KAUST, Saudi Arabia, 2016 B.Sc. Mathematics, University of Cyprus
<> data analysis (6)

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Amal Alghamdi

Nizar Ajeeb

Full Time Researcher Full at King Abdullah University of Science and Technology.

L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

1 min read ·
Paola Crippa (presently at University of Newcastle, UK) is one of the winners of the highly contested 2015 L'oreal-UNESCO UK & Ireland For Women In Science Fellowships (FWIS) for the article "Modelling particulate matter pollution from vegetation fires in South-East Asia".

Superior seismic imaging for better drilling

1 min read ·
Software could transform underground imaging of fossil fuel reserves by providing unprecedented detail in record time.

SRSI student success story at Ibdaa 2022

1 min read ·
Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.
<> data assimilation (6)

Particle Approximations For Partially Observed Fluid Dynamics Models

Professor Dan Crisan, Mathematics, Imperial College London

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KAUST

Fluid dynamics models are ubiquitous in a multitude of applications. One of the most important applications of fluid dynamics models is numerical weather prediction. Modern numerical weather prediction combines sophisticated nonlinear fluid dynamics models with increasingly accurate high-dimensional data. This process is called data assimilation and it is performed every day at all major operational weather centers across the world. Data assimilation (DA) requires massive computing capabilities as realistic atmosphere-ocean models typically have billions of degrees of freedom. I will give a short overview of the ongoing research that aims to drastically decrease the required DA computational effort by reducing the dimension of the models involved and using stochastic perturbations to account for the unresolved scales. The incorporation of observation data is done by using particle approximations suitably adapted to solve high-dimensional problems.

Is probability theory the only viable solution to modelling statistical information?

Prof. Jeremie Houssineau, Department of Statistics, University of Warwick.

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KAUST

Many alternatives to the probabilistic modelling of information have been proposed since the birth of modern Statistics; yet, few have been successfully applied to the complex inference problems that modern Statisticians are faced with.

Raul Tempone

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Håkon A. Hoel

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.

Habib Toye Mahamadou Kele

Brief Biography Habib Toye is a PhD candidate at King Abdullah University of Science and Technology (KAUST), in the Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE) under the supervision of Professor Ibrahim Hoteit. His research interests include Ensemble ocean forecasting, High performance computing and Data assimilation. Research Interest Habib's research focuses on developing and implementing efficient ensemble Kalman filters for data assimilation into a high resolution model of the Red Sea. He has developed a fault-free DART-MITgcm ensemble assimilation
<> discrete differential geometry (6)

Isotropic Geometry and Applications in Geometric Computing

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B3 L5 R5209

This thesis addresses challenges in computational design and fabrication, particularly in the geometry of architectural gridshell structures and their approximation, by solving nonlinear optimization problems.

Towards Flexible Polyhedral Nets

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B4 L5 R5209

This thesis research provides a comprehensive classification of flexible geometric nets of arbitrary size in both Euclidean and isotropic geometries, revealing that only two distinct classes exist in each setting and demonstrating their relationship to mechanical design.

Pirahmad Olimjoni

Khusrav Yorov

Khusrav's research focuses on geometry and its application in real life, especially differential geometry, discrete differential geometry, and isotropic geometry.

Hui Wang

Postdoctoral Research Fellow, Computer Science
Hui Wang was a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Helmut Pottmann until June 2023 Education and Early Career Before joining VCC, Hui Wang was a joint PhD student of Prof. Helmut Pottmann’s Applied Geometry group in TU Wien for two years (2017.9-2019.10). She got her PhD in Dalian University of Technology (DUT) in 2020. Hui Wang's current research interests include architectural geometry, discrete differential geometry, geometry processing, and computational design. Education

Anthony Ramos

Anthony Ramos is an MS/PhD Student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Helmut Pottman in his research group. Education and Early Career Anthony have obtained Bachelor's degree in Physicist from Yachay Tech University, Urcuqui, Ecuador - government project with the idea of making an international university for the country, from which he was part of the second generation of students. Research Interest Anthony Ramos is passionate about geometry and programming. He loves to bring problems to
<> extreme computing (6)
ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.

Superior seismic imaging for better drilling

1 min read ·
Software could transform underground imaging of fossil fuel reserves by providing unprecedented detail in record time.

Award-winning algorithm aids observation

1 min read ·
Researchers at the Extreme Computing Research Center (ECRC) at KAUST have developed a new algorithm that uses supervised machine learning to control a small high-speed deformable mirror inside the exoplanet imaging camera to compensate for atmospheric turbulence in the Subaru Telescope, which has an 8.2 meter diameter.

Quieter wind beneath the wings

1 min read ·
The ability to efficiently simulate the noise generated by wings and propellers promises to accelerate the development of quieter aircraft and turbines.

A brainwave from social networks

1 min read ·
The statistics used to understand social networks reveal the diversity of functional connections in the brain.
Sameh Abdulah

Sameh Abdulah

Dr. Sameh Abdulah specializes in HPC, scalable algorithms, and climate modeling, with innovations receiving the ACM Gordon Bell Prize for Climate Modelling.
<> Finite elements (6)

A Dichotomy of Continuous Finite Element Spaces and Its Application to Energy-Conservative Galerkin Methods for Nonlinear and Dispersive Wave Equations

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B1 L3 R3119

This talk illustrates why energy-conservative Galerkin methods for nonlinear and dispersive wave equations achieve optimal convergence rate with odd-degree polynomials.

Towards Finite Element Tensor Calculus

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B1 L3 R3119

In this talk we provide an overview of some efforts to develop finite elements for tensors, motivated by the need for structure-preserving and compatible discretization.

High-order non-conforming finite elements for the Stokes equation

Stefan Sauter, Professor, Institute of Mathematics, University of Zurich

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B2 L5 R5220

We consider non-conforming discretizations of the stationary Stokes equation in two and three dimensions by Crouzeix-Raviart type elements. The original definition in the seminal paper by M. Crouzeix and P.-A. Raviart in 1973 is implicit and also contains substantial freedom for a concrete choice.

Finite element approximations for variable-order fractional diffusion problems

Prof. Wenyu Lei, University of Electronic Science and Technology of China

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B4 L5 R5209

We propose a finite element scheme for fractional diffusion problems with varying diffusivity and fractional order. A number of challenges are encountered when discretizing such problems. The first is the heterogeneous kernel singularity in the fractional integral operator. The second comes from the dense discrete operator with its quadratic growth in memory footprint and arithmetic operations. To address these challenges, we propose a strategy that decomposes the system matrix into three components. The first is a sparse matrix that handles the singular near-field separately and is computed by adapting singular quadrature techniques available for the homogeneous case to the case of spatially variable order.

Rolf Krause

Professor Rolf Krause is an experienced researcher and educator specializing in designing and analyzing algorithms to solve complex problems in scientific computing and machine learning.

Daniele Boffi

Professor Boffi is a leading international expert in mixed finite elements and the approximation of eigenvalue problems arising from partial differential equations.
<> LLM (6)

Yanda Meng

Assistant Professor, Bioengineering
Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

KAUST advances scalable AI through global collaboration

1 min read ·
KAUST brings global experts together to advance scalable AI and tackle the challenges of training large models.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B1 L3 R3119

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Ahmar Husain

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Muhammad Samiullah

Cyber Security, FPGA security, AI/ML security
<> metamaterials (6)

Waves Functional Materials - 2019-09-15

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B9 L2 H1 R2322

Wave functional materials are artificial materials that can control wave propagation as wished. In this talk, I will give a brief review of the progress of wave functional materials and reveal the secret behind the engineering of these materials to achieve desired properties.

Ying Wu awarded Phononics Young Investigator 2017

1 min read ·
Prof. Ying Wu received the 2017 Phononics Young Investigator Award as the Phononics community gathered in Changsha, China, for the 4th International Conference on Phononic Crystal/Metamaterials, Phonon Transport/Coupling, and Topological Phononics, on June 4-9 2017.

Zeguo Chen

Master, Nanjing University 2014 Bachelor, Nanjing University 2011 Research Interests Wave propagation in Metamaterials and Phononic Crystals.

Ying Wu

Professor Wu works on theoretical modeling and numerical simulations of classical wave propagation in complex media, including metamaterials, metasurfaces, topological materials and random systems. She is recognized by effective medium theory, perturbation theory for periodic structures with resonances.

Xiujuan Zhang

Dr. Xiujuan Zhang is an Associate Professor at School of Engineering and Applied Sciences, Department of Materials Science and Engineering, Nanjing University. Her research focuses on acoustic metamaterials and topological physics.

Mohamed D. Farhat

Research Scientist, Waves in Complex Media
Mohamed Farhat is currently a Research Scientist at King Abdullah University of Science and technology (KAUST), Thuwal, Saudi Arabia.
<> quantum computing (6)

Abderrahman Kantos

Abderrahman Kantos is currectly a Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Before beginning his doctoral studies, Abderrahman completed an internship at RTE (Réseau de Transport d’Électricité), working on quantum computing and the development of quantum algorithms. At École Polytechnique, he participated in the Projet Scientifique Collectif (PSC). His team ranked among the top 10 out of more than 100 teams, under the project title

Dimension Liftings for Quantum Computation of Partial Differential Equations and Related Problems

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B1 L3 R3119

This lecture presents a systematic framework for developing quantum simulation algorithms for general differential equations, using dimension lifting to transform nonlinear and time-dependent problems into forms solvable by simulating the Schrödinger equation.

The Earliest Arrival of Quantum Advantage

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B9 L2 R2325

This talk outlines a "quantum first" strategy for future supercomputers, integrating QPUs, GPUs, and CPUs to optimize energy efficiency and accelerate scientific computing while addressing the current challenges and projecting the maturation of quantum computing by leveraging classical supercomputing advancements.

CEMSE Tutorial in Quantum Computing

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B19 L3 H1

With the end of the Moore’s law, it became imperative to find new principles for computing that can avoid the current physical limitations. Among the promising approaches is Quantum Computing which has resulted in substantial national investments in research and development in this area by many nations. This first tutorial is designed to target scientists, engineers and mathematicians who are interested in this rapidly growing field but have not yet invested enough time in learning about it.

Quantum Computing with Xanadu

Catalina Albornoz, Quantum Community Manager at Xanadu

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KAUST

Xanadu is a Canadian quantum computing company with the mission to build quantum computers that are useful and available to people everywhere. Xanadu is one of the world’s leading quantum hardware and software companies and also leads the development of PennyLane, an open-source software library for quantum computing and application development.

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.
<> Wireless Communications (6)

Racha Zayni

Visiting Researcher, Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Chaabane Mankai

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Connecting the Unconnected at the KAUST 6G Summit

1 min read ·
The inaugural 6G Summit on "Connecting the Unconnected" took place on Tuesday, October 27, and featured a host of international researchers and experts in the areas of Internet and Communication Technology (ICT) and Wireless Communications System (WCS).

KAUST Distinguished Professor Mohamed-Slim Alouini honored with IEEE accolades

1 min read ·
Professor Mohamed-Slim Alouini has been awarded both the 2019 Institute of Electrical and Electronics Engineers (IEEE) Communication Society Communication Theory Technical Committee (CCTC) Technical Achievement Award, and the 2020 IEEE Vehicular Technology Society James Evans Avant Garde Award. The CEMSE Division founding faculty member received the awards for his contributions in the modeling, design, performance evaluation, and optimization of wireless communication systems and networks.

Shaping a 6G future

1 min read ·
This coming decade promises to be yet another revolutionary one in how we communicate. In a world that is only beginning to embrace 5G technology, several scientific institutions, governments, telecom companies, and phone manufacturers worldwide are beginning to research the development possibilities of its successor—6G technology.
<> applied statistics (5)

Efficiency Assessment of Approximated Spatial Predictions for Large Datasets

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KAUST

Our suggested criteria are more useful for the determination of tuning parameters for sophisticated approximation methods of spatial model fitting. To illustrate this, we investigate the trade-off between the execution time, estimation accuracy, and prediction efficiency for the TLR method with intensive simulation studies and suggest proper settings of the TLR tuning parameters.

Solvers for Coupled Problems in Porous Media Science

Arne Naegel

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B9 L2 R2322

No abstract is available.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.

Grigory Malinovsky

Grigory Malinovsky is a M.S./Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik. Before joining KAUST, Grigory obtained a bachelor's degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Russia. Research Interests Grigory's research interests include machine learning, optimization, and applied statistics. Specifically, he is interested in the theoretical aspects of optimization algorithms that can be applied to real machine-learning problems. Education Profile B.A., Major in Applied
<> bayesian inference (5)

Manifold Markov Chain Monte Carlo Methods for Bayesian Inference In Diffusion Models

Prof. Alexandros Beskos, the Dept of Statistical Science, University College London (UCL)

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KAUST

Bayesian inference for nonlinear diffusions, observed at discrete times,is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology - borrowing ideas from statistical physics and computational chemistry - for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process

KAUST professors receive Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Mohamed Eddaoudi, KAUST distinguished professor of chemical science and director of the University's Advanced Membrane & Porous Materials Center, recently received the Abdul Hameed Shoman Award for Arab Researchers for Renewable Energy, an award presented annually by the Abdul Hameed Shoman Foundation. Omar Knio, KAUST professor of applied mathematics and computational science, was also honored by the Abdul Hameed Shoman Foundation this year as a joint recipient of the Shoman Award for Mathematical Modeling with Dr. Shaher Mohammed Ahmad Momani.

Raul Tempone

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.
<> brain science (5) <> Computational finance (5)

Abderrahmene Ben Romdhane

KAUST-CEMSE-The-International-Research-Conference-on-Multi-Grid-and-Multi-Scale-Methods-in-Computational-Science

KAUST International Research Conference on Multi-Grid and Multi-Scale Methods in Computational Science, IMG 2025

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Building 19 and 20

The International Conference on Multigrid and Multiscale Methods in Computational Science (IMG) offers a dynamic forum for researchers to share advances in multigrid, multilevel and multiscale computational techniques, as well as their applications across scientific domains.

Soumaya Elkantassi

Soumaya Elkantassi obtained a Master's degree in Applied mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Soumaya's research interests included Statistics, Probability Theory and Stochastic Processes, and Computational Finance. Education Profile M.Sc. in Applied mathematics and Computational Sciences, Statistics and Data Analysis, King Abdullah University of Science and Technology (2015 - 2017). Engineering in Economics and

Raul Tempone

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Gaukhar Shaimerdenova

Gaukhar Shaimerdenova is a consultant at the Stochastic Numerics Research Group (STOCHNUM) of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Gaukhar's research interests include ​Stochastic Differential Equations, Numerical Analysis, Data Assimilation, Multilevel/Multi-index Monte Carlo methods, Ensemble Kalman Filtering and Computational Finance. Selected Publications Rached, N.B., Schwerin, E.V., Shaimerdenova, G. and Tempone, R., 2026. Importance sampling for rare event tracking within the ensemble Kalman filtering framework
<> COVID-19 (5)

A Rapid, Accurate and Machine-agnostic Segmentation and Quantification Method for CT-based COVID-19 Diagnosis

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KAUST

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this talk, I will introduce our recent work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon three innovations: 1) an embedding method that projects any arbitrary CT scan to a same, standard space, so that the trained model becomes robust and generalizable; 2) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 3) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

CT-based COVID-19 Diagnosis and Prognosis through Novel AI Model Development

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B2 L5 R5220

During the recent pandemic, urgent advances have been made by the scientific community in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis. In this talk, I will introduce our work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources.

A shock to the system

1 min read ·
The global, multifarious challenge posed by the COVID-19 pandemic has scientists tapping their wide-ranging fields of expertise to attack the problem on many fronts. Answering the call from KAUST President Tony Chan, and coordinated by the University's leadership team, KAUST researchers making up the Rapid Research Response Team (R3T) are turning this crisis into an opportunity to innovate.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

KAUST scientists develop virus mutation tracker

1 min read ·
​KAUST scientists have developed a user-friendly COVID-19 mutation tracking system, useful for authorities and scientists to quickly detect variants in their region, allowing them to make speedy policy and public health decisions.
<> Cyber Security (5) <> energy (5)

Numerical Methods for Energy Minimisation: Application to Machine Learning Algorithms

Prof. Charalambos Makridakis, Director of the Institute of Applied and Computational Mathematics of FORTH, Greece

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B2 L5 R5220

In this talk, we discuss problems and numerical methods arising in the calculus of variations and energy minimization. Among numerous applications, energy minimization is a core element of Machine Learning algorithms. Within the field of nonlinear PDEs, the calculus of variations has received a lot of attention from the analysis point of view. Although quite interesting and challenging, the numerical analysis of these problems is much less developed.

Panel Discussion: A Roadmap for Bringing to Earth the Energy of the Stars

Prof. Amitava Bhattacharjee, Dr. Michael Zarnstorff, Dr. Spencer Pitcher, Mr. Richard Carty

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B20 Auditorium

“Practical fusion power is thirty years off and always will be.” Most people are instantly enraptured at the thought that a single glass of water will provide enough fusion fuel for their lifetime – if only it could be safely unlocked – and most of us have heard, as well, that we should not set our watches for when this will occur.

Lecture 1: Hidden Symmetries and Fusion Energy

Prof. Amitava Bhattacharjee, Astrophysical Sciences, Princeton University

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B20 Auditorium

The most compelling transformational use of magnetically confined, high-temperature plasma is to realize sustained fusion energy. The tokamak, which is the leading magnetic confinement concept in the world today, first realized in 1958, has the geometry of a torus and toroidal symmetry, giving it good confinement properties. Nevertheless, the tokamak has a number of unresolved stability issues related to its current-carrying plasma that may be obstacles to its ultimate success. In contrast, in the stellarator, the confining magnetic field is mostly produced by external current-carrying coils.

Powering the bottom line

1 min read ·
A risk-based optimization scheme boosts confidence and profitability for future mixed-technology power plants.

Solving the grandest of challenges

1 min read ·
Around the world, scientists, researchers and engineers seek to develop sustainable alternatives to the burning of fossil fuels. Some explore the kinetic energy of wind, gravity or water. Others capture excited electrons using photosensitive materials. William Tang, a principal research physicist at the Princeton Plasma Physics Laboratory at Princeton University, wants to power the world using the chemical reaction that stars run on—nuclear fusion—and he believes deep learning is a key.
<> environment (5) <> extreme weather (5) <> Federated learning (5)

Reem Arnaout

Visiting student at King Abdullah University of Science and Technology (KAUST).

KAUST advances scalable AI through global collaboration

1 min read ·
KAUST brings global experts together to advance scalable AI and tackle the challenges of training large models.

Rafal Szlendak

Rafał Szlendak is an MS/PhD student in the AMCS program under the supervision of Prof. Peter Richtarik. His biggest passions are research, weightlifting and travelling. His career path is motivated by his endless passion for self-development and discovery. Education and Early Career Rafał Szlendak has obtained his Bachelor's degree in Mathematics and Statistics from University of Warwick Research Interest In his research Rafał focuses on stochastic gradient descent algorithms for large-scale machine learning problems. His primary goal is developing state-of-the-art methods with sound

Laurent Condat

Senior Research Scientist, Computer Science
I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Grigorii Malinovskii

Ph.D. Student, Computer Science
I am a PhD student in Applied Mathematics and Computational Science at King Abdullah University of Science and Technology, advised by Prof. Peter Richtárik, working on optimization theory and its applications in Machine Learning. My hobbies include drums, fitness, martial arts, football, and basketball.
<> Finite element methods (5)

Approximation and Optimization for Neural Networks

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In this talk, we consider new connections between the approximation and optimization of neural networks. Instead of relying on excessive over-parametrization to achieve zero training loss, we identify good minima by comparison with established approximation bounds.

Finite element approximation of Stokes equations with non-smooth data

Lucia Gastaldi, Professor Department of Mathematics, University of Brescia

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B1 L4 R4102

Finite element approximation of Stokes equations with non-smooth data

Umberto Zerbinati

Steven Dufour

Steven Dufour is a Former Visiting ​Associate Professor from École Polytechnique de Montréal, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Steven's research activities revolve around the development of numerical methodologies for the modeling of free surface flows that can be found in various industrial applications. The main challenge associated with the numerical modeling of multifluid flows is to accurately locate the interfaces between each fluid. More complex flows, such as the flow of

Najwa Alshehri

Professional Interests Beyond academic and research pursuits, Najwa is deeply committed to mentoring and empowering female students to pursue careers in science and research. At KAUST, she engaged in initiatives that cultivate talent in mathematics, AI, and deep learning. Additionally, she placed significant emphasis on developing her leadership and entrepreneurial skills through specialized programs and certifications along with other trainings in Key Performance Indicators (KPI), Total Quality Management (TQM), and strategic planning.
<> gamma-convergence (5)

Asymptotic properties of composites

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B9 L2 R2322 H1

In this talk, I will start with an overview of my research to date. Then, I will address in more detail the study of the asymptotic behavior of a two-dimensional variational model within finite crystal plasticity for high-contrast bilayered composites. More precisely, we consider materials arranged into periodically alternated thin horizontal strips of an elastically rigid component and a softer one with one active slip system. The energies arising from our modeling assumptions are of integral form, featuring linear growth and non-standard differential constraints.

Part 1: Introduction to homogenisation and Gamma-Convergence

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B2 B3 A0215

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 1: (1) The homogenisation problem; (2) Gamma-convergence - definition, main properties, examples, comparison with pointwise limit; (3) Lower semicontinuity, lower semicontinuous envelope; (4) Coercivity

Part 2: Introduction to homogenisation and Gamma-Convergence

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B1 L2 R2202

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 2: (1) Integral functionals with p growth, 1 p 1; The localisation method of Gamma-convergence; Integral representation.

Part 3: Introduction to homogenisation and Gamma-Convergence

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B1 L4 R4214

We will start by introducing the notion of Gamma-convergence and its main properties. We will then show how this can be applied to the study of homogenization of integral functionals. This includes, as a particular case, the homogenization of uniformly elliptic PDEs. Lecture 3: (1) Compactness; (2) Application of the compactness result; 93) The homogenisation theorem.

Rita A. Ferreira

Rita's research activities intersect applied and pure mathematics and are driven by applications to physical and socio-economic sciences and engineering.
<> genomics (5) <> healthcare (5) <> ISAC (5)
kaust-cemse-ece-ccsl-changhao-he

Changhao He

kaust-cemse-ece-ccsl-yousef-awad

Yousef Awad

kaust-cemse-ece-ccsl-bumin-kagan-yildirim

Bumin Kagan Yildirim

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022
<> nonlinear PDEs (5)

The invisible architect

4 min read ·
Most people never see the mathematics running beneath modern life. CEMSE Professor Miguel Urbano is making sure it works when it matters most.

Rafayel Teymurazyan

Rafayel Teymurazyan works on nonlinear partial differential equations and free boundary problems arising from applications in bio-technologies, chemical engineering, financial mathematics, and optimal design, among others.

Miguel Urbano

Professor Urbano’s research interests are in regularity theory for nonlinear partial differential equations, in particular of singular or degenerate type, arising from different applications, like phase transitions, flows in porous media or semi-supervised learning. He is also interested in free boundary problems, focusing on understanding the local behaviour of weak solutions and the geometric properties of interfaces.

Diogo Gomes

Professor Diogo Gomes, a distinguished expert in Applied Mathematics and Computational Science at KAUST, leverages advanced mathematics – including partial differential equations, numerical methods and mean-field game models – to solve complex problems in social sciences, economics and finance

Athanasios Tzavaras

KAUST Professor of Applied Mathematics and Computational Science Athanasios (Thanos) Tzavaras specializes in mathematical modelling, analysis and computation of partial differential equations pertaining to fluid mechanics or modeling of materials.
<> phononics (5)

Acoustic metamaterials and metasurfaces for controllable wave manipulation

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B1 L2 R2202

I will provide an overview on recent researches on acoustic and elastic metamaterials and metasurfaces for controllable wave manipulation, we are developing in my group in the University of Lorraine. I first will present some advances related to low-frequency acoustic and vibration shielding/absorption making use of metamaterials1,2 and metasurfaces3,4, and describe the added value that such artificial engineered materials can bring to consider some innovative applications.

Ying Wu awarded Phononics Young Investigator 2017

1 min read ·
Prof. Ying Wu received the 2017 Phononics Young Investigator Award as the Phononics community gathered in Changsha, China, for the 4th International Conference on Phononic Crystal/Metamaterials, Phonon Transport/Coupling, and Topological Phononics, on June 4-9 2017.

Zeguo Chen

Master, Nanjing University 2014 Bachelor, Nanjing University 2011 Research Interests Wave propagation in Metamaterials and Phononic Crystals.

Ying Wu

Professor Wu works on theoretical modeling and numerical simulations of classical wave propagation in complex media, including metamaterials, metasurfaces, topological materials and random systems. She is recognized by effective medium theory, perturbation theory for periodic structures with resonances.

Mohamed D. Farhat

Research Scientist, Waves in Complex Media
Mohamed Farhat is currently a Research Scientist at King Abdullah University of Science and technology (KAUST), Thuwal, Saudi Arabia.
<> Python (5) <> regularity (5)

A very short introduction to regularity theory for linear elliptic equations - Part 1

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 1 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 3

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 3 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 4

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L3 R3119

Mini Course Part 4 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

A very short introduction to regularity theory for linear elliptic equations - Part 2

Professor Jose Urbano, Department of Mathematics at University of Coimbra, Portugal

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B1 L2 R2202

Mini Course Part 2 of 4. The course is a very short introduction to regularity for linear elliptic pdes of second order. We start with equations with regular coefficients and the difference quotient method of Nirenberg. We then treat the case of coefficients that are merely measurable and bounded, putting forward the basics of De Giorgi-Nash-Moser theory. If time permits, we present some characterizations of Hölder spaces which are very useful in regularity theory.

Rafayel Teymurazyan

Rafayel Teymurazyan works on nonlinear partial differential equations and free boundary problems arising from applications in bio-technologies, chemical engineering, financial mathematics, and optimal design, among others.
<> Reinforcement Learning (5)

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering
Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Mohammed Alwagait

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Aznaur Aliev

M.S. Student

We used Reinforcement Learning; but did it work?

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.

Inference for Longitudinal Data After Adaptive Sampling

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
<> robotics (5)

Aigerim Nurbayeva

Postdoctoral Research Fellow, Electrical and Computer Engineering

Team KAUST places 3rd at the Mohamed Bin Zayed International Robotics Challenge

1 min read ·
A team from the King Abdullah University of Science and Technology (KAUST) competed in the inaugural Mohamed Bin Zayed International Robotics Challenge (MBZIRC), held in Abu Dhabi, UAE, March 2017, and took a 3rd place finish in the ground robotics challenge.

Renzo Caballero

Education 2022.05.24 Ph.D. Research Proposal: "Theory and Dynamics of Self-Repair Systems" Committee Members: Prof. Eric Feron, Prof. Shinkyu Park, Prof. Diogo Gomez, Prof. David Keyes, Prof. Taous-Meriem Laleg 2019.06 - 2023.12 Ph.D. in Applied Mathematics and Computational Sciences, KAUST, KSA 2017.08 - 2019.06 M.Sc. in Applied Mathematics and Computational Sciences, KAUST, KSA ​2010.02 - 2016.06 B.Sc. in Electrical Engineering, Universidad de la Republica, Montevideo, Uruguay Professional Profile 2021.08 - 2021.12 Teaching Assistant, CEMSE, KAUST, KSA 2017.01 - 2017.04 Models Analyst, UTE

Jun Chen

Postdoctoral Research Fellow, Electrical and Computer Engineering
Dr. Jun Chen received his Ph.D. degree in mechanical engineering from Temple University, USA in 2021, and his M.S. degree in electrical and computer engineering from Stevens Institute of Technology, USA in 2017. Before that, he received his B.S. degree from Hefei University of Technology, China in 2015. His research interests lie in robotics, distributed multi-robot systems, robot learning, etc.

Bacem Ben Nasser

Bacem Ben Nasser is a postdoctoral researcher fellow in the Electrical Engineering department within the CEMSE division. He obtained his Ph.D. degree in Sciences and Technology-Mathematics from the University of Sfax, Faculty of Sciences of Sfax, Tunisia, where he worked in Lyapunov and control theories, nonlinear integral inequalities and time scale calculus. In January 2017, he was affiliated with the University of Kairouan, Tunisia, at the Higher Institute of Applied Sciences and Technology of Kairouan as assistant professor of applied mathematics. Dr. Ben Nasser joined the research group
<> spatial statistics (5)

CEMSE New Faculty: David Bolin, Associate Professor of Mathematical Statistics

1 min read ·
David Bolin is an associate professor of mathematical statistics who joined the KAUST Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division earlier this month from the Department of Mathematical Sciences at the University of Gothenburg. Before joining KAUST, Bolin received both his Ph.D. degree in mathematical statistics and M.S.c in engineering mathematics from Lund University, Sweden, in 2012 and 2007, respectively. Upon completing his Ph.D., he spent one year at Umeå University, Sweden, working as a postdoctoral fellow before moving to the Chalmers University of Technology. In 2016, Bolin became an associate professor of mathematics at the University of Gothenburg, where a year later he received the title of Docent in mathematical statistics.

CEMSE Division student spotlight: Gaurav Agarwal

1 min read ·
Gaurav Agarwal firmly believes in the age-old adage that patience is a virtue. Without patience, life’s challenges cannot be overcome, and a steadfast belief in perseverance has served the statistics Ph.D. student well throughout his academic career.

Haavard Rue

Program Chair, Statistics
Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.

Gaurav Agarwal

Ph.D., Statistics
Gaurav Agarwal obtained his Ph.D. degree in Statistics from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Ying Sun in the Environmental Statistics group. His research focused on developing statistical models for dealing with the issues concerning the environment. Bringing data to life, with statistics and programming excites him. Besides research, he likes to dance and work out. Research Interest Gaurav's research interests included Multivariate quantiles, Non-parametric statistics, Functional data analysis, and Spatial Statistics. Awards and

David Keyes

David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.
<> Statistics of extremes (5)
Simon Nik

Simon Nik

Postdoctoral Research Fellow, Statistics
Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany, with a thesis entitled " Stochastic modeling and statistical analyses of non-negative data and times series". He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany. He joined KAUST as a postdoc in December 2024. Research interests Simon's current research interests focus primarily on extreme

The Lambert Award for Young Statisticians Goes To Raphael Huser

1 min read ·
Today Raphael Huser, Assistant Professor at King Abdullah University of Science and Technology (KAUST), received the Lambert Award for Young Statisticians for his contribution in modeling the distribution of extreme weather events.

KAUST Assistant Professor Raphaël Huser receives American Statistical Association award

1 min read ·
KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Rustam Bekishev

M.S. Student, Statistics
Rustam Bekishev obtained his MSc. in Statistics at the King Abdullah University of Science and Technology in December 2018. After his time at KAUST, he got a job as a Senior Business Analyst at the Applied Economics Research Center ( AERC), Kazakhstan. Areas of expertise and current scientific interests Rustam Bekishev's research interests focus on statistics of extremes.

Raphaël Huser

Associate Professor, Statistics
Professor Huser develops novel statistical methodology and machine learning solutions to model and predict extreme events in various applications ranging from climate and earth sciences to finance and neuroscience.
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Professor Marc Genton named Distinguished Professor of Statistics at KAUST

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Marc G.Genton, Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), has been appointed by KAUST President Jean-Lou Chameau to Distinguished Professor of Statistics.

Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

1 min read ·
Marc Genton, KAUST distinguished professor of statistics, and his former postdoctoral fellow Stefano Castruccio, have jointly won the 2017 Wilcoxon Award for best applications paper in Technometrics. Technometrics is a journal of statistics for the Physical, Chemical and Engineering Sciences, and is published quarterly by the American Society for Quality and the American Statistical Association. The award announcement was made at the 61st Annual Fall Technical Conference (FTC) in Philadelphia from October 5 to 6, 2017. Genton and Castruccio's paper was entitled "Compressing an ensemble with

Nanovisualization Research Group is hiring

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Nanovisualization Research Group is hiring scientific staff on the levels of Research Scientist, Postdoctoral Fellow, and Graduate Student. Follow the links to apply for the respective position: Research Scientist position Postdoctoral Fellow Graduate Student

Marc Genton received an ISI Service Award 2019

1 min read ·
Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.
<> Acoustics (4) <> aerodynamics (4)

Luca Galimberti

Luca is a Ph.D. candidate in Applied Mathematics at KAUST, he focuses on the development of numerical methods for aeroacoustics and aerodynamics of aeronautical applications.

A Variational Theory of Aero-hydrodynamics

Haithem Taha, Associate Professor

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The Euler equation does not possess a unique solution for the flow over a two-dimensional object. This problem has serious repercussions in aerodynamics; it implies that the inviscid aero-hydrodynamic lift force over a two-dimensional object cannot be determined from first principles; a closure condition must be provided. The Kutta condition has been ubiquitously considered for such closure in the literature, even in cases where it is not applicable (e.g. unsteady).

A race against time

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The theme of the University's 2019 Winter Enrichment Program (WEP) was "time." It represented an opportune moment for KAUST to welcome members of the McLaren Group to campus to discuss the recently signed extreme performance research partnership between KAUST and McLaren.

Better racing car design through an industry partnership

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The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.
<> Analysis of PDE's (4)
Analysis and PDEs Workshop-2025

Workshop on Analysis and PDEs

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Join us for the Workshop on PDEs to explore advances, challenges and methods in the analysis and computation of partial differential equations.

Simone Portaro

Simone Portaro is a Ph.D. Student in the Computer, Electrical, Mathematical Sciences and Engineering (CEMSE) department at the Applied Mathematics and Computational Sciences (AMCS) program under the supervision of Professor Peter Markowich at King Abdullah University of Science and Technology. Simone received his B.S. degree in Industrial Engineering from the University of Catania, Italy in 2017. In 2020, he received his M.S. degree in Mathematical Engineering from Politecnico di Torino, Italy. Research interests Nonlinear Partial Differential Equations, Analysis-Modeling of biological network

Myrto Galanopoulou

Short Bio Dr. Myrto Galanopoulou received a B.Sc. and a M.Sc. in Applied Mathematics in 2015 from the University of Crete and a Ph.D. from KAUST in 2020. During her Ph.D. studies she worked on the problem of polyconvex thermoelasticity. In September 2021 she joined the School of Mathematical And Computer Sciences, Heriot-Watt University, as a Research Associate Fellow. Dr. Galanopoulou's research interests lie in the area of analysis of partial differential equations with applications in fields as continuum physics or mechanics. Currently she is working on the mathematical theory of

Aseel AlNajjar

<> cognitive radio systems (4)

Prof. Alouini selected as an IEEE Communications Society Distinguished Lecturer for 2016 and 2017

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Congratulations to Prof. Alouini for being selected to give a Distinguished Lecture for 2016 and 2017 in IEEE Communications Society

Marie Sklodowska-Curie Fellowships in Photonics @ CEMSE – call 4 - 2018

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A group of professors working in the area of photonics at KAUST, most of them with CEMSE Division, offer hosting of outgoing Marie Sklodowska-Curie Fellowships in their research groups within the EU H2020 COFUND Postdoctoral Fellowship Programme MULTIPLY.

A breath of fresh information to help diagnosis

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A communications concept could pinpoint a person infected with a deadly, contagious virus in the middle of a crowded airport.

Mohamed-Slim Alouini

Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering
KAUST is a fertile place for any researcher who wants to develop rigorous computational and engineering methods to solve real-world problems. Its unique support of science allows the most advanced scientific revolution.
<> combinatorial optimization (4)

Professor Mikhail Moshkov’s new book published by Springer

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Professor Mikhail Moshkov’s new book, “Computation Trees: A Generalization of Decision Trees,” has been published by Springer. Moshkov’s book is devoted to the study of deterministic and nondeterministic computation trees. Computation trees are a natural generalization of decision trees: in addition to the one-place predicate-type operations (attributes) used in decision trees, computation trees can use multi-place predicate and function operations. These models arise in areas such as combinatorial optimization, computational geometry, and classification or prediction tasks, particularly when
Decision Trees Versus Systems of Decision Rules

New book by KAUST Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov and Professor Mikhail Moshkov published by Springer

1 min read ·
New book by Ph.D. students Kerven Durdymyradov, Azimkhon Ostonov, and Professor Mikhail Moshkov explores decision trees and rule systems using rough set theory.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

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Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.
<> computational methods (4)

CEMSE Welcomes new Professor: Dr. Ying Sun

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Ying Sun is an Assistant Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE). She joined KAUST after one-year service as an assistant professor in the Department of Statistics at the Ohio State University.

Professor Sun receives the Environment Early Investigator Award 2017 for her outstanding contribution in environmental statistics

1 min read ·
Ying Sun, Professor of Statistics in the Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE) of the King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, has been awarded the 2017 Early Investigator Award by the American Statistical Association’s Section on Statistics and the Environment (ENVR).

Using Statistical Models to Solve Environmental Problems

1 min read ·
Joining the faculty of King Abdullah University of Science and Technology (KAUST) six years ago gave statistics Professor Ying Sun a unique career opportunity: the chance to build and lead her own research group solving real-world problems.

SRSI student success story at Ibdaa 2022

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Faisal Fadi Hasan Almulla, who worked last year under the mentorship of Dr. Fouzi Harrou and Professor Ying Sun as part of the annual KAUST Saudi Research Science Institute (SRSI) summer program, won two awards at the National Olympiad for Scientific Creativity — Ibdaa 2022.
<> computational predictions (4)

Professor Marc Genton named Distinguished Professor of Statistics at KAUST

1 min read ·
Marc G.Genton, Professor of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), has been appointed by KAUST President Jean-Lou Chameau to Distinguished Professor of Statistics.

Professor Marc Genton and former postdoctoral fellow win the 2017 Wilcoxon Award

1 min read ·
Marc Genton, KAUST distinguished professor of statistics, and his former postdoctoral fellow Stefano Castruccio, have jointly won the 2017 Wilcoxon Award for best applications paper in Technometrics. Technometrics is a journal of statistics for the Physical, Chemical and Engineering Sciences, and is published quarterly by the American Society for Quality and the American Statistical Association. The award announcement was made at the 61st Annual Fall Technical Conference (FTC) in Philadelphia from October 5 to 6, 2017. Genton and Castruccio's paper was entitled "Compressing an ensemble with

Marc Genton received an ISI Service Award 2019

1 min read ·
Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.

Marc G. Genton

Al-Khawarzmi Distinguished Professor, Statistics
Professor Genton’s pioneering research on large-scale spatial and temporal data has profoundly affected the field of environmental statistics. He is an expert in spatial and spatio-temporal statistics with applications to environmental problems.
<> computational statistics (4)

Andrea Vanessa Rocha

Research Specialist, Statistics

Ying Sun

Professor, Statistics
Professor Ying Sun leads a research group focusing on environmental statistics. She is at the forefront of developing advanced models and methodologies in the fields of spatio-temporal statistics, functional data analysis and process monitoring.

Raul Tempone

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

David Bolin

Professor, Statistics
David Bolin is an expert on stochastic processes, random fields and spatial statistics. His main research focus is on stochastic partial differential equations and their applications in statistics.
<> diffusive phenomena (4)

Professor Peter Markowich Has Been Awarded The "John Von Neumann Visiting Chair" At The Technical University Of Munich

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Professor Peter Markowich has been awarded the "John Von Neumann Visiting Chair" at the Technical University of Munich. The John von Neumann Visiting Professorship allows outstanding scientists to spend a semester at the TUM Center for Mathematics.

Professor Markowich named Fellow of the Institute of Mathematics and its Applications

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Prof. Peter Markowich has been named a Fellow of the Institute of Mathematics and its Applications (IMA). IMA Fellowships are awarded to individuals who have shown demonstrable achievement and competence in the development or application of mathematics.

Professor Markowich elected to the European Academy of Sciences and Arts

1 min read ·
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich has been named a member of the European Academy of Sciences and Arts (EASA). He will officially be inducted as a member at the organization's annual inauguration ceremony in early 2023.

Peter Markowich

Al-Khawarzmi Distinguished Professor, Applied Mathematics and Computational Science
A distinguished professor of applied mathematics and computational science, Dr. Markowich is the author of over 200 publications in leading international journals and circa 14,000 citations.