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<> diffusive phenomena (4)

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.

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 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.
<> environmental applications (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).

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.

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.
<> imaging apparatus (4)

Prof. Andrea Fratalocchi receives appreciation from Nature Publications as one of its best reviewers

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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

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.
<> integrated photonics (4)
KAUST-CEMSE-ECE-IPL-Nikita-Kondratyev

Nikita Kondratyev

Mikhail Mishevskii

Mikhail received the B.S. degree in applied mathematics and physics from Novosibirsk State, Novosibirsk, Russia and received a master's degree with honors from the Skolkovo Institute of Science and Technology (Skoltech). His research interests include: nonlinear optics, integrated photonics, ultrafast lasers, frequency comb generation. During his time at Skoltech participated in the Skoltech Translational Research and Innovation Program industrial project to create a pulsed submicron laser for pumping a single-photon quantum light source. Mikhail awarded with Scholarship of the President of

Aleksandr Movsisian

Aleksandr received his bachelor’s degree in 2023 in Physics from Moscow State University (MSU, ranked first in Russia) and completed a master’s degree in Physics with honors at the same institution. He has over two years of research experience in quantum optics, optomechanics, and quantum measurement theory as part of the LIGO group at MSU. His work has focused on squeezed states of light, measurement topologies, and methods to overcome the Standard Quantum Limit, resulting in two first-author publications in JOSA B and Optics Letters. Aleksandr also gained hands-on experimental experience at

Aram Mkrtchyan

Postdoctoral Research Fellow, Electrical and Computer Engineering
<> inverse problems (4)

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 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.

Yuri Ashrafyan

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Yuri Ashrafyan is a Postdoctoral Research Fellow working with Professor Diogo Gomes at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yuri graduated from Yerevan State University with a Ph.D. in Mathematics in 2018. After that, he worked as Assistant Lecturer before joining KAUST in 2019 as a Postdoctoral Research Fellow with Prof. Dominik Michels in the Visual Computing Center (VCC). He is currently a Postdoctoral Research Fellow with Professor Diogo Gomes in the CEMSE division. Research Interest He’s interested in his research in Spectral Theory

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.
<> isoperimetric inequality (4)

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 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 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.
<> Large Language Models (4) <> Monte Carlo (4)

Monte Carlo based Inference for State-space Models

Sumeetpal Singh, Reader, Engineering Statistics, Department of Engineering, University of Cambridge

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KAUST

State-space models are widely used for the analysis of time-series data but full Bayesian inference is still elusive. I will review some applications of state-space models and recent endeavours towards efficient Monte Carlo sampling, in particular using Particle filtering and more recent Particle Markov Chain Monte Carlo methods. I will discuss the theoretical scalability of these methods with respect to the length of the observed time-series. Our theoretical results on their efficiency align well with many documented instances of their effectiveness based on extensive numerical studies. The talk will conclude with some open challenges to be pursued.

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting

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B1 L3 R3119

In this paper, we propose a detailed theoretical study of one of these algorithms known as the split Gibbs sampler. Under regularity conditions, we establish explicit convergence rates for this scheme using Ricci curvature and coupling ideas. We support our theory with numerical illustrations.

Multigrid Monte Carlo Revisited: Theory and Bayesian Inference

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B1 L3 R3119

Abstract The fast simulation of Gaussian random fields plays a pivotal role in many research areas such as Uncertainty Quantification, data science and spatial statistics. In theory, it is a well understood and solved problem, but in practice the efficiency and performance of traditional sampling procedures degenerates quickly when the random field is discretised on a grid with spatial resolution going to zero. Most existing algorithms, such as Cholesky factorisation samplers, do not scale well on large-scale parallel computers. On the other hand, stationary, iterative approaches such as the

Nadhir Ben Rached

Introduction Nadhir went to RWTH Aachen University, Aachen, Germany as a ​​Postdoctoral Research Fellow​. Education and early career Nadhir obtained his M.S and Ph.D. degree in Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia between 2012 - 2018. Research Interest Efficient Monte Carlo approach for rare event simulation Accurate Performance Analysis of Wireless Communication Systems Computational Finance Stochastic Differential Equations Numerical Analysis Selected Publications Graduation Project, Adaptive Monte
<> Multi-agent systems (4)

Ansar Khangeldin

M.S. Student
Ms/PhD Electrical and Computer Engineering student. Have a background in Applied Mathematics, Control Theory, Machine Learning Research and Development, Multi-Agent Reinforcement Learning. Main research interests are application and enhancement of CV methods in autonomous systems (AVs, Robotics, UAVs).

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

Eder D. Baron Prada

Ph.D. student at KAUST, my research interests include game theory, optimization, and control of multi-agent systems.

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
<> Multigrid (4)
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.

Neural Operators: Theory, Architecture, and Applications for PDEs

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B1 L3 R3119

Abstract Neural operator methods provide a novel approach for solving or learning the complex mappings from parameters to solutions arising from intricate physical systems. In this talk, I will cover the foundational aspects of neural operators, encompassing both theoretical frameworks and algorithmic developments, including some well-known neural operator architectures. Additionally, I will share our recent work on applying the neural operator method to multiscale partial differential equations (PDEs). To tackle the challenges of multiscale PDEs, we have developed a neural operator with a

Multigrid Monte Carlo Revisited: Theory and Bayesian Inference

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B1 L3 R3119

Abstract The fast simulation of Gaussian random fields plays a pivotal role in many research areas such as Uncertainty Quantification, data science and spatial statistics. In theory, it is a well understood and solved problem, but in practice the efficiency and performance of traditional sampling procedures degenerates quickly when the random field is discretised on a grid with spatial resolution going to zero. Most existing algorithms, such as Cholesky factorisation samplers, do not scale well on large-scale parallel computers. On the other hand, stationary, iterative approaches such as the

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.
<> Nonlinear Optics (4)
KAUST-CEMSE-ECE-IPL-Nikita-Kondratyev

Nikita Kondratyev

Mikhail Mishevskii

Mikhail received the B.S. degree in applied mathematics and physics from Novosibirsk State, Novosibirsk, Russia and received a master's degree with honors from the Skolkovo Institute of Science and Technology (Skoltech). His research interests include: nonlinear optics, integrated photonics, ultrafast lasers, frequency comb generation. During his time at Skoltech participated in the Skoltech Translational Research and Innovation Program industrial project to create a pulsed submicron laser for pumping a single-photon quantum light source. Mikhail awarded with Scholarship of the President of

Aram Mkrtchyan

Postdoctoral Research Fellow, Electrical and Computer Engineering

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.
<> numerical methods for PDE's (4)

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

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.

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

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
<> Numerical Optimization (4)

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.

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

Muxingzi Li

Muxingzi Li was a master student in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Muxingzi Li grew up in Wenzhou, China, a city famed for its entrepreneurs and the most difficult dialect in the country. She received the bachelor degree in Mathematics from Oxford University in 2015 and joined the Visual Computing Center, King Abdullah University of Science and Technology (KAUST) as a MS/PhD student in Applied Mathematics and Computational Science. She
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.
<> numerical PDEs (4) <> numerical simulations (4)

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.

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.
<> remote sensing (4)

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.

Gleb Ludnikov

To strive and explore, to uncover and never give in.

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
<> renewable energy (4)

Reduced-Order High Fidelity Simulations of Reacting Flows Using Low Dimensional Manifolds and Machine Learning

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B1 R3119

This talk will provide an overview of historical developments in mathematical and computational approaches to reduced order models for accelerated high fidelity reacting flow simulations in modern computing hardware.
aerial-shot-of-wind-generator-turbines

Accounting for extreme weather to boost energy system reliability

1 min read ·
A computer model that factors in extreme weather events offers insights to improve the design of NEOM’s integrated renewable power, water and heating sectors.

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.

Hussain Shibli

MS Degree, Applied Mathematics and Computational Sciences ,King Abdullah University of Science and Technology. Director general of the National Renewable Energy Data Center in King Abdullah City for Atomic and Renewable Energy (KACARE)
<> sensors (4) <> statistical learning (4)

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

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B2/3 A0215

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

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.

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.
<> stochastic algorithm (4)

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

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B2/3 A0215

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

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.

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
<> stochastic algorithms (4)

Christian Bayer

Christian Bayer's research focuses on financial mathematics and stochastic numerics, including modelling stock indices and numerical approximation of stochastic optimal control problems.

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.

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)

-

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.

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)

-

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.
<> Stochastic Modeling (4)

KAUST Ph.D. student wins Society for Industrial and Applied Mathematics award

1 min read ·
Chiheb Ben Hammouda, a KAUST Ph.D. student in the University's Stochastic Numerics Research Group, recently won the best poster award at the Society for Industrial and Applied Mathematics (SIAM) Conference on Financial Mathematics & Engineering (FM19) held at the University of Toronto from June 4 to 7. His winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," is one of several research projects carried out by Ben Hammouda under the supervision of KAUST Professor Raul Tempone.

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

Fabián Crocce

Postdoctoral Research Fellow, Stochastic Numerics Research Group
Fabián Crocce worked as a Postdoctoral Fellow​​ at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Partial Differential Equations, Probability Theory, and Stochastic Processes Distributed Strategic Learning, Stochastic Modeling. Optimal stopping for diffusions, both one-dimensional and multi-dimensional. Representation of the solution by the Green kernel of the process. His research interests also included optimal stopping problems for jump-processes and infinite-horizon problems. Fourier methods

Chiheb Ben Hammouda

Chiheb Ben Hammouda 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). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic
<> stochastic processes (4) <> student award (4)

KAUST Ph.D. student wins American Statistical Association paper competition

1 min read ·
Sabrina Vettori, a Ph.D. student co-supervised by KAUST Distinguished Professor Marc Genton, who leads the Spatio-Temporal Statistics & Data Science group, and KAUST Assistant Professor Raphaël Huser, who leads the Extreme Statistics Research Group, won the 2017 Student Paper Competition sponsored by the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA). ​ Eligible papers focused on environmental statistics, and the four competition winners presented their papers at an ENVR-sponsored session at the 2017 Joint Statistical Meetings (JSM), held from

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

KAUST master’s degree student wins best poster award at Data Science Summer School

1 min read ·
Samuel Horváth, a master's degree student in the University's statistics and optimization M.S./Ph.D. program, recently won a best poster award at the second edition of the Data Science Summer School (DS3). The event took place in Paris from June 25 to 29, and is one of the largest global data science summer schools. It was co-organized by the Data Science Initiative of École Polytechnique and DATAIA Institute. The primary goal of DS3 was to provide a series of courses and practical sessions covering the latest advances in the field of data science. Five hundred students, postdoctoral fellows

Ph.D. student Michał Mańkowski wins poster award at the 18th Annual American Society of Transplant Surgeons Symposium

2 min read ·
KAUST Ph.D. student Michał Mańkowski won the Poster of Distinction Award at the American Society of Transplant Surgeons (ASTS) 18th Annual State of the Art Winter Symposium earlier this year. Photo by Sarah Munshi.
<> University of Jyvaskyla in Finland (4)

Part 3: The isoperimetric inequality. Stability and related topics

-

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

-

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 2: The isoperimetric inequality. Stability and related topics

-

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

-

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.
<> weather (4) <> wireless communication (4)

Reem Arnaout

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

Nadhir Ben Rached

Introduction Nadhir went to RWTH Aachen University, Aachen, Germany as a ​​Postdoctoral Research Fellow​. Education and early career Nadhir obtained his M.S and Ph.D. degree in Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia between 2012 - 2018. Research Interest Efficient Monte Carlo approach for rare event simulation Accurate Performance Analysis of Wireless Communication Systems Computational Finance Stochastic Differential Equations Numerical Analysis Selected Publications Graduation Project, Adaptive Monte
<> aeroacoustics (3)

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.

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
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.
<> Asymptotic Performance Analysis (3)

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.

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.

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.
<> Bayesian Estimation (3) <> Bayesian Statistics (3) <> climate projections (3)
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.

Mathematical modeling of the stochastic climate system

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B9 L2 H1

​Author of more than 290 journal and conference publications, Professor Stenchikov's research interests are in multi-scale modeling of environmental processes and numerical methods; global climate change, climate downscaling, atmospheric convection; assessment of anthropogenic impacts and geoengineering; air-sea interaction, evaluating environmental consequences of catastrophic events like volcanic eruptions, nuclear explosions, forest and urban fires; and air pollution, transport of aerosols, chemically and optically active atmospheric tracers, their radiative forcing and effect on climate.

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.
<> climatology (3) <> complex models (3)

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

-

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

-

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.
<> computational design (3)

Geometry and Computational Design

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KAUST

An important stream of research in computational design aims at digital tools which support users in realizing their design intent in a simple and intuitive way, while simultaneously taking care of key aspects of function and fabrication. Such tools are expected to shorten the product development cycle through a reduction of costly feedback loops between design, engineering and fabrication. The strong coupling between shape generation, function and fabrication is a rich source for the development of new geometric concepts, with an impact to the original applications as well as to geometric theory. This will be illustrated at hand of applications in architecture and fabrication with a mathematical focus on discrete differential geometry and geometric optimization problems.

Geometry and Computational Design of Equilibrium Structures

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B1 L3 R3119

Freeform structures play a prominent role in contemporary architecture. In order to stay within reasonable cost limits, computational shape design has to incorporate aspects of structural analysis and fabrication constraints. The talk discusses solutions to important problems in this area. They concern the design of polyhedral surfaces with nearly rectangular faces, polyhedral surfaces in static equilibrium, the smoothest visual appearance of polyhedral surfaces and the closely related problem of finding material-minimizing forms and structures. From a methodology perspective, there is an interplay of geometry, mechanics and optimization. Classical subjects such as isotropic geometry, a simple Cayley-Klein geometry, play a role as well as most recent developments in discrete differential geometry. We also show how practical requirements have led to new results and open problems in geometry.

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
<> computational simulations (3) <> convex optimization (3)

Additive Schwarz Methods for Convex Optimization - Theory and Applications

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B5 L5 R5209

This talk is devoted to additive Schwarz methods for convex optimization. First, we propose an abstract framework for additive Schwarz methods for convex optimization. The framework's flexibility allows it to handle composite optimization problems and inexact local solvers. Moreover, it establishes a sharp convergence theory that agrees with the classical theory when addressing linear problems.

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.

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
<> copulas (3) <> data (3) <> data mining (3) <> decision trees (3)

Professor Mikhail Moshkov’s new book published by Springer

1 min read ·
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.

New Book "Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
New book "Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions" by Fawaz Alsolami, Mohammad Azad, Igor Chikalov, and Mikhail Moshkov is published by Springer
<> dispersive waves (3)

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.

Nonlinear and dispersive waves: Theory, computations and applications

Dr. Dimitrios Mitsotakis, Senior Lecturer, Victoria University of Wellington

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KAUST

The study of waves in fluids is one of the most significant branches of fluid mechanics. Part of this study is the theory of nonlinear and dispersive waves which has recently emerged and is still under development. Nonlinear and dispersive waves appear in fluids of any form and have significant role in the fields of oceanic waves (surface and internal), atmospheric modelling, electromagnetism, nonlinear optics, ultra-cold matter and even in blood flow problems. In this presentation we will review relevant applications, such as tsunami waves, the El Nino southern oscillation, blood flow in arteries and solitons propagating in optical fibres. Mathematical modelling techniques for deriving equations that describe such phenomena will be introduced in the context of surface water waves. We will also review the minimum required theoretical background in order to proceed with safe numerical simulations. Finally, we will discuss the numerical modelling of such problems where methods such as standard and mixed Galerkin / Finite element methods are of central focus. We close this presentation by showcasing a topic of much current interest, namely, the development of modern mathematical models for nonlinear and dispersive waves by combining machine learning techniques with classical methodologies.

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.
<> Distributed algorithms (3) <> Electroencephalograms (3) <> embedded systems (3) <> extreme statistics (3) <> FD-MIMO (3) <> fluid-structure interactions (3)

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.

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.

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.
<> flux-corrected transport (3)

Locally Discrete Maximum Principle DG finite element method for the transport equation

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B1 L4 R4214

In this talk we propose a Flux Corrected Transport (FCT)-like stabilization suitable for high-order Discontinuous Galerkin (DG) finite element discretizations. As a model problem we consider the multi-dimensional transport equation. The method guarantees that the solution satisfies a local Discrete Maximum Principle (DMP). A solution is said to satisfy a DMP locally if any degree of freedom is bounded with respect to the solution at the previous time step in some defined neighborhood.

Algebraic flux correction for finite element discretizations of hyperbolic conservation laws

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B1 L4 R4214

In this talk, we review some recent advances in the analysis and design of algebraic flux correction (AFC) schemes for hyperbolic problems. In contrast to most variational stabilization techniques, AFC approaches modify the standard Galerkin discretization in a way which provably guarantees the validity of discrete maximum principles for scalar conservation laws and invariant domain preservation for hyperbolic systems. The corresponding inequality constraints are enforced by adding diffusive fluxes, and bound-preserving antidiffusive corrections are performed to obtain nonlinear high-order approximations. After introducing the AFC methodology and the underlying theoretical framework in the context of continuous piecewise-linear finite element discretizations, we present some of the limiting techniques that we use in high-resolution AFC schemes. This presentation is based on joint work with Dr. Manuel Quezada de Luna (KAUST) and other collaborators.

Manuel Quezada De Luna

Research Scientist, Numerical Mathematics
Dr. Suleyman Ulusoy earned both his Ph.D in Mathematics in 2007 and his Master of Science in Applied Mathematics in 2003 from Georgia Institute of Technology in Atlanta, Georgia, USA. He earned both of his Bachelor’s degrees in Mathematics and Mathematics Education from the Middle East Technical University in Ankara, Turkey in 2000. He did postdocs in University of Oslo and University of Maryland. He is currently a faculty member in American University of Ras Al Khaimah.
<> generalized Sobolev inequalities (3)

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.

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.
<> generative ai (3) <> Generative AI and LLMs (3) <> geometry processing (3) <> haps (3)

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
H

Hussein Al-Shatri

I chose KAUST because of its reputation for excellence in research and its recent emphasis on applied research. With my extensive background in both academic research and industrial development, I am excited to contribute my experience and ambition. My goal is to foster new ideas and develop innovative prototypes in the field of wireless communications, which will undoubtedly enhance people's lives.
<> Homogenization (3) <> Hyperbolic PDEs (3)

On Strong Stability of Runge-Kutta Methods

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B1 L4 R4214

Runge-Kutta methods are classical and widespread techniques in the numerical solution of ordinary differential equations (ODEs). Considering partial differential equations, spatial semidiscretisations can be used to obtain systems of ODEs that are solved subsequently, resulting in fully discrete schemes. However, certain stability investigations of high-order methods for hyperbolic conservation laws are often conducted only for the semidiscrete versions.

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

Xiaokai Huo

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Short Bio Xiaokai Huo received a bachelor degree in Mathematics from Tsinghua University, China in 2012 and a Ph.D degree in Mathematics from Tsinghua University China, 2017. He was a Postdoctoral fellow in the Applied PDE group at KAUST in the period October 2017 - October 2020. In October 2020 he joined the Institute of Analysis and Scientific Computing, Vienna University of Technology as a Postdoctoral Fellow. Xiaokai Huo's research interests lie in the area of Hyperbolic PDEs, Fluid Mechanics and Non-equilibrium Thermodynamics. Education Profile Ph. D. Mathematics, Tsinghua University
<> Isotropic Geometry (3) <> LiDAR (3) <> Mathematical Biology (3)
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.

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.
<> mathematical optimization (3)

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.

Optimization for Deep Learning

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B5 L5 R5209

Abstract The field of optimization for machine learning has undergone significant changes in recent years with deep learning models increasing in scale and fine-tuning taking a more prominent role. In this presentation, I will share a perspective on the direction of changes in the field and highlight interesting research directions. I will provide real-world examples of what practitioners want from optimization methods to train deep networks at scale. I will then present my recent work on adaptive methods, such as Adam and Adagrad, and explain how we can estimate the learning rate for these

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.
<> MATLAB (3) <> Metasurfaces (3)

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.

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.

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.
<> mixed finite elements (3)

Theory and Implementation of Novel Numerical Methods for Multiphysics Interface Problems

-

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.

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.

Daniele Boffi

Professor Boffi is a leading international expert in mixed finite elements and the approximation of eigenvalue problems arising from partial differential equations.
<> monte carlo algorithms (3)

Overcoming the curse of dimensionality: from nonlinear Monte Carlo to deep learning

Arnulf Jentzen, Professor, Applied Mathematics Münster: Institute for Analysis and Numerics, University of Münster

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KAUST

In this talk we prove that suitable deep neural network approximations do indeed overcome the curse of dimensionality in the case of a general class of semilinear parabolic PDEs and we thereby prove, for the first time, that a general semilinear parabolic PDE can be solved approximatively without the curse of dimensionality.

Meet KAUST prospective student: Miguel Ángel Álvarez Ballesteros

1 min read ·
Miguel Ángel Álvarez Ballesteros is a physics graduate from the Universidad Industrial de Santander, Bucaramanga, Colombia. Miguel will join KAUST this fall as a M.S./Ph.D. candidate and member of the KAUST Computational Probability research group under the supervision of Professor Ajay Jasra.

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.
<> Multi-face flows (3)

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

-

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

-

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
<> nanomaterials (3) <> Neural Networks (3) <> nlp (3) <> Nonlinear Partial Differential Equation (3)

Gravitational Collapse of Self-similar stars

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B1 L4 R4102

The Euler-Poisson equations give the classical model of a self-gravitating star under Newtonian gravity. It is widely expected that, in certain regimes, initially smooth initial data may give rise to blow-up solutions, corresponding to the collapse of a star under its own gravity. In this talk, I will present recent work with Yan Guo, Mahir Hadzic and Juhi Jang that demonstrates the existence of smooth, radially symmetric, self-similar blow-up solutions for this problem. I will also comment on the stability of the obtained solution. At the heart of the analysis is the presence of a sonic point, a singularity in the self-similar model that poses serious analytical challenges in the search for a smooth solution.

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

Shuaikun Wang

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
Short Bio Shuaikun Wang received his bachelor's degree in Mathematics from University of Science and Technology of China in 2012 and a Ph.D degree in Mathematics from City University of Hong Kong in 2016. He was a postdoc in City University of Hong Kong in 2016-2017 and a Postdoctoral Research Fellow in the Applied PDE group at KAUST from September 2017 - December 2020. Currently he is an Associate Professor in the School of Mathematics, Shandong University, Jinan, China. Shuaikun Wang's research interests lie in the area of applied analysis, nonlinear partial differential equations and
<> optimal control (3)

Salim Ksous

Epidemiological modeling and optimal control

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KAUST

Compartmental epidemiological models are one of the simplest models for the spread of a disease. They are based on statistical models of interactions in large populations and can be effective in the appropriate circumstances. Their application historically and in the present pandemic has sometimes been successful and sometimes spectacularly wrong. In this talk I will review some of these models and their application. I will also discuss the behavior of the corresponding dynamical systems, and discuss how the theory of optimal control can be applied to them. I will describe some of the challenges in using such a theory to make decisions about public policy.

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.
<> parallel computing (3)

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.

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.

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.
<> probability theory (3)

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.

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.

Fabián Crocce

Postdoctoral Research Fellow, Stochastic Numerics Research Group
Fabián Crocce worked as a Postdoctoral Fellow​​ at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Partial Differential Equations, Probability Theory, and Stochastic Processes Distributed Strategic Learning, Stochastic Modeling. Optimal stopping for diffusions, both one-dimensional and multi-dimensional. Representation of the solution by the Green kernel of the process. His research interests also included optimal stopping problems for jump-processes and infinite-horizon problems. Fourier methods
<> Programming language (3) <> Quantitative finance (3)

Christian Bayer

Christian Bayer's research focuses on financial mathematics and stochastic numerics, including modelling stock indices and numerical approximation of stochastic optimal control problems.

KAUST Ph.D. student wins Society for Industrial and Applied Mathematics award

1 min read ·
Chiheb Ben Hammouda, a KAUST Ph.D. student in the University's Stochastic Numerics Research Group, recently won the best poster award at the Society for Industrial and Applied Mathematics (SIAM) Conference on Financial Mathematics & Engineering (FM19) held at the University of Toronto from June 4 to 7. His winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," is one of several research projects carried out by Ben Hammouda under the supervision of KAUST Professor Raul Tempone.

Chiheb Ben Hammouda

Chiheb Ben Hammouda 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). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic
<> reliability testing (3)

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.
<> reproducibility issues (3)

Reproducibility in Computer Science - Part 1

-

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.
<> scientific enterprise (3)

Reproducibility in Computer Science - Part 1

-

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

-

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

-

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.
<> Scientific Machine Learning (3)

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.

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.
<> signal analysis (3) <> simulation (3) <> software development (3)

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.

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.
<> solar energy (3)

Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

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B9 H2 R2325

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the

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.

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.
<> spatio-temporal data analysis (3)

Geospatial Data Science for Public Health Surveillance

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B9 L2 R2325

This talk will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance, highlighting challenges related to data biases and availability.

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

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.
<> Statistical computing (3) <> Statistical Modeling (3) <> stochastic (3)

Recent developments for stochastic processes and their applications

Ahmed Kebaier, Associate Professor, Laboratory Analysis, Geometry and Application (LAGA), University Sorbonne Paris Nord (USPN)

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KAUST

In this talk, we present our theoretical results recently developed around various types of applications. These results cover asymptotic and non-asymptotic analysis, functional limit theorems, concentration inequalities, and Malliavin calculus techniques elaborated for several classes of stochastic processes. We showcase through concrete examples the role of these results in taking up various challenges at the cutting-edge of modern applied probability.

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.

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
<> Stochastic Optimal Control (3)

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

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

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.
<> supercomputers (3) <> Université de Montréal (3)

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.
<> Virtual Element Method (3)

C1-Conforming Virtual Element Method (VEM) for Optimal Control of Oseen Equations with a Stream-Function Formulation

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B1 L3 R3426

This talk presents a C¹-conforming Virtual Element Method for the optimal control of generalized Oseen equations, detailing its discretization strategies, theoretical error estimates, and numerical validation on polygonal meshes.

Parallel multilevel solvers for virtual element discretizations of saddle point problems

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B1 L3 R3119

In this seminar, we will present our work on Virtual Element Method (VEM) approximations. The Virtual Element Method is a recent numerical technique for solving partial differential equations on computational grids constituted by polygonal or polyhedral elements of very general shape. This work aims to develop effective linear solvers for general order VEM approximations of three-dimensional scalar elliptic equations in mixed form and Stokes equations. To this end, we consider block algebraic multigrid preconditioners and balancing domain decomposition by constraints (BDDC) preconditioners. The latter allows us to use conjugate gradient iterations, albeit the algebraic linear systems arising from the discretization of the differential problems are indefinite, ill-conditioned, and of saddle point nature.

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.
<> Wave functional materials (3) <> acoustic waves (2) <> acoustic-elastic metamaterials (2) <> adaptive methods (2) <> Advanced Perception (2) <> agricultural productivity (2) <> AI for healthcare (2) <> AI4Science (2) <> algorithmics (2) <> applied geometry (2)

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

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