Events

May 10 - May 16, 2026

• 

  Time Series Clustering: Pattern Recognition, Forecasting, and Amortized Inference

  Ángel López Oriona, Postdoctoral Research Fellow, Statistics

  May 14, 12:00 - 13:00

  B9 R2325

  Time Series Pattern Recognition forecasting statistical inference

  This talk presents innovative time series clustering techniques, highlighting a quantile-based approach for analyzing locally stationary data, a predictive framework for enhanced forecasting, and the use of amortized inference to overcome traditional algorithmic limitations.

Apr 26 - May 2, 2026

• 

  Robust and Fuzzy Methods for High-Dimensional Time Series Clustering and Forecasting

  Ziling Ma, Ph.D. Student, Statistics

  Apr 30, 12:00 - 13:00

  B9 R2325

Apr 19 - Apr 25, 2026

• 

  Rare-Event Simulation Methods for Outage Probability in GSC/MRC Systems under Rician Fading

  Mahmoud Hassan Ghazal , Ph.D. Student, Electrical and Computer Engineering

  Apr 23, 12:00 - 13:00

  B9 R2325

  Statistics of extremes Outage Probability Applied Probability

• 

  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

  Apr 19 - 29, All day

  Auditorium 0215 between B4 & B5

  Workshops

  stochastic algorithm numerical analysis statistical learning

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

Apr 12 - Apr 18, 2026

• 

  Efficient Numerical Methods for Scalable Bayesian Inference

  Lisa Gaedke-Merzhäuser, Postdoctoral Research Fellow, Statistics

  Apr 16, 12:00 - 13:00

  B9 R2325

  numerical methods Efficient Bayesian Inversion Automated and scalable algorithms

Apr 5 - Apr 11, 2026

• 

  Data Analytics to Outcome Impact for Transplantation and Clinical Practice

  Apr 9, 10:00 - 16:00

  B3 R5220

  Workshops

  AI for healthcare Mathematical modeling smart health bioinformatics Data Analytics

  Leveraging national data analytics and computational tools to advance transplantation and clinical practice.

Mar 8 - Mar 14, 2026

• 

  Extrapolated Linear Multistep Methods

  Lajos Lóczi, Research Scientist, Applied Mathematics and Computational Science

  Mar 12, 12:00 - 13:00

  B9 L2 R2325

  ODEs PDEs numerical methods linear multistep methods LMMs

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

  Advances in Multiscale Hierarchical Decomposition Methods for Image Restoration

  Luminita Vese, Professor of Mathematics, University of California, Los Angeles (UCLA)

  Mar 10, 14:30 - 15:30

  B1 L3 R3119; Zoom Meeting 99254591389

  This talk will present the multiscale hierarchical decomposition method (MHDM) for image restoration and scale separation, building on the framework introduced by Tadmor, Nezzar, and me.
• 

  Mathematical Modelling of Solar Cells

  Theodoros Katsaounis, Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC)

  Mar 10, 11:00 - 12:30

  B1 L3 R3119

  solar cells Mathematical modeling PDEs numerical PDEs

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

Mar 1 - Mar 7, 2026

• 

  Remote Sensing and Agroinformatics Insights in Saudi Arabia Using Machine Learning

  Ting Li, Postdoctoral Research Fellow, Environmental Science and Engineering

  Mar 5, 12:00 - 13:00

  B9 L2 R2325

  remote sensing machine learning sustainable agricultural agricultural productivity

  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.
• 

  Approximation and Optimization for Neural Networks

  Gerrit Welper, Assistant Professor, Mathematics, University of Central Florida (UCF)

  Mar 4, 16:00 - 17:00

  Zoom Meeting 95807131415

  Neural Networks optimization deep neural networks Finite element methods

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

  Structure Preserving Methods for Schrodinger Type of Equations

  Theodoros Katsaounis, Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC)

  Mar 3, 14:30 - 15:30

  B1 L3 R3119

  Schrödinger equation cosmology waves

  This talk presents a class of structure preserving methods for Schrodinger type of equations with applications in the generation of rogue waves and cosmology.

Feb 22 - Feb 28, 2026

• 

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

  Tan Bui-Thanh, Professor, Endowed William J. Murray, Jr. Fellow in Engineering No. 4, Oden Institute for Computational Engineering & Sciences, Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin (UT Austin)

  Feb 26, 14:30 - 15:30

  B1 L3 R3119

  Scientific Machine Learning SciML Scientific Deep Learning SciDL deep learning machine learning digital twins uncertainty quantification Computational mathematics

  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.
• 

  Graphpcor: Prior for Correlation Matrices

  Elias Teixeira Krainski, Research Scientist, Statistics

  Feb 26, 12:00 - 13:00

  B9 L2 R2325

  correlation Bayesian Estimation expert knowledge integration

  This talk introduces a scalable, graph-based framework for modeling correlation matrices that integrate expert-informed priors.
• 

  Computing Heteroclinic Orbits for Dynamical Systems

  Theodoros Katsaounis, Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC)

  Feb 23, 11:00 - 12:30

  B1 L3 R3119

  Dynamical Systems heteroclinic orbits Numerical Modeling

  This course provides an overview of the most well known methods for computing heteroclinic orbits for dynamical systems.

Feb 15 - Feb 21, 2026

• 

  C1+alpha Regularity for the Fractional p-Laplacian

  David De Jesus, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Feb 19, 12:00 - 13:00

  B9 L2 R2325

  Mathematical modeling

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

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

  Dimitrios Mitsotakis, Reader/Associate Professor, Engineering Mathematics, School of Mathematics and Statistics, Victoria University of Wellington (VUW)

  Feb 17, 14:30 - 15:30

  B1 L3 R3119

  dispersive waves optimization Finite elements

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

Feb 8 - Feb 14, 2026

• 

  Data-driven Anomaly Detection in Industrial Processes

  Fouzi Harrou, Senior Research Scientist, Statistics

  Feb 12, 12:00 - 13:00

  B9 L2 R2325

  anomaly detection multivariate statistics artificial intelligence AI

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

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

  Jongho Park, Research Scientist, Applied Mathematics and Computational Science

  Feb 10, 14:30 - 15:30

  B1 L3 R3119

  nonlinear models algorithms iterative methods

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

Feb 1 - Feb 7, 2026

• 

  On the Modeling and Approximation of Phase Transitions in Elasticity

  Georgios Grekas, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Feb 5, 12:00 - 13:00

  B9 L2 R2325

  Phase transitions elasticity mathematical modelling

  This talk explores the mathematical modeling of phase transitions in elasticity, drawing motivation from observed phenomena in crystalline solids and biomaterials.

Jan 25 - Jan 31, 2026

• 

  Energy-Efficient and Sustainable Spatial Modeling Using GPU Computing

  Sameh Abdulah, Senior Research Scientist, Applied Mathematics and Computational Science

  Jan 29, 12:00 - 13:00

  B9 L2 R2325

  HPC

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

  The Sharpness Condition for Constructing Finite Element From a Superspline

  Qingyu Wu, Ph.D. Student, Mathematics, Peking University

  Jan 28, 14:00 - 15:00

  B1 L3 R3119

  In this talk, I will discuss the sharpness conditions for constructing Cʳ conforming finite element spaces from superspline spaces on general simplicial triangulations and introduce the concept of extendability for pre-element spaces, which unifies both superspline and finite element spaces under a common framework.
• 

  KAUST Research Conference on Mathematical and Data Sciences

  Jan 26 - 28, All day

  KAUST Campus

  data science scientific computing Computer science

  Theory and applications of data science.
• 

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

  Alfio Quarteroni, Emeritus Professor, Politecnico di Milano and EPFL

  Jan 25, 14:00 - 15:00

  B9, L2, R2322

  Computational mathematics numerical methods Scientific Machine Learning scientific computing applied mathematics

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

Jan 18 - Jan 24, 2026

• 

  Programmable Wavefunction Dynamics for Topological and Nonlinear Acoustic Control

  Ze-Guo Chen, Associate Professor School of Advanced Manufacturing, Nanjing University, China

  Jan 21, 10:00 - 11:00

  B1 L3 R3119

  topological phenomena

  This talk presents a unified route to topological and nonlinear acoustic control through engineered time-dependent modulation of on-site parameters, couplings, gain/loss, and external driving.

Jan 11 - Jan 17, 2026

• 

  Isotropic Geometry and Applications in Geometric Computing

  Khusrav Yorov, Ph.D. Student, Applied Mathematics and Computational Science

  Jan 15, 10:30 - 12:00

  B3 L5 R5209

  Isotropic Geometry geometry processing discrete differential geometry

  This thesis addresses challenges in computational design and fabrication, particularly in the geometry of architectural gridshell structures and their approximation, by solving nonlinear optimization problems.

Dec 7 - Dec 13, 2025

• 

  Mean Field Games: From Many-Player Games to PDEs

  Melih Ucer, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Dec 11, 12:00 - 13:00

  B9, L2, R2325

  mean field games hamilton–jacobi equations monotone operator theory

  This talk will introduce the basic concepts of mean field games, beginning with the mean field limit that describes systems with infinitely many infinitesimal players.
• 

  Stochastic Interacting Particle Methods and Generative Learning for Multiscale PDEs

  Jack Xin, Distinguished Professor, Department of Mathematics, School of Physical Sciences, University of California, Irvine

  Dec 9, 14:30 - 15:30

  B1 L3 R3119

  PDEs machine learning

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

  Tales of a Spur Hunter: The Wandering Spur

  Michael Peter Kennedy, Full Professor, School of Electrical and Electronic Engineering, University College Dublin

  Dec 7, 12:00 - 13:00

  B9 L2 R2325

  Prof. Michael Peter Kennedy presents the decade-long journey to diagnose and fix 'wandering spurs' in frequency synthesizers.

Nov 30 - Dec 6, 2025

• 

  Nuclear Fusion Powered by AI and HPC

  Vladimir Pimanov, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Dec 4, 12:00 - 13:00

  B9 L2 R2325

  AI artificial intelligence Fusion simulation

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

  Will AI Replace Professors?

  Pavel Pevzner, Ronald R. Taylor Chair and Distinguished Professor, Computer Science and Engineering, University of California, San Diego

  Dec 4, 12:00 - 13:00

  B2/B3 L0 A0215

  This talk explores Massive Adaptive Interactive Texts (MAITs) as a pioneering AI technology that aims to replace the one-size-fits-all lecture model with a responsive and scalable system for individualized instruction.
• 

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

  Bertram Düring, Professor of Mathematics Mathematics Institute, University of Warwick

  Dec 2, 14:30 - 15:30

  B1 L3 R3119

  PDEs

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

  Mathematical Imaging in the Era of AI

  Carola-Bibiane Schönlieb, Professor, DAMTP, University of Cambridge

  Dec 2, 13:30 - 14:30

  B1 L23 R3119

  AI optimization

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

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

  Harpal Singh, Ph.D. Student, Department of Mathematics, Indian Institute of Technology Roorkee

  Dec 1, 14:30 - 15:30

  B1 L3 R3426

  Virtual Element Method

  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.

Nov 23 - Nov 29, 2025

• 

  Finite Element Approximation of Eigenvalue Problems in Mixed Form

  Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering

  Nov 27, 12:00 - 13:00

  B9 L2 R2325

  This talk will discuss the finite element approximation of the eigenvalues associated with the Maxwell system.
• 

  Phenomenological and Mechanistic Modeling of Gene Regulatory Dynamics in Cell Differentiation Using Single-Cell Data

  Juan Pablo Bernal Tamayo, Ph.D., Applied Mathematics and Computational Science

  Nov 26, 10:00 - 12:00

  B24 (Innovation Cluster) L3 R3302

  This dissertation develops integrated phenomenological and mechanistic modeling frameworks that maintain biological interpretability while capturing regulatory dynamics during cellular differentiation.
• 

  KAUST Workshop on Distributed Training in the Era of Large Models

  Nov 24 - 26, All day

  Auditorium between B4 & 5, L0, R0215

  machine learning Distributed algorithms generative models ML

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

Nov 16 - Nov 22, 2025

• 

  Geospatial Data Science for Public Health Surveillance

  Paula Moraga, Assistant Professor, Statistics

  Nov 20, 12:00 - 13:00

  B9 L2 R2325

  statistical methods geospatial data analysis health surveillance Public Health spatio-temporal data analysis

  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.
• 

  The Sharpness Condition for Constructing Finite Element From a Superspline

  Qingyu Wu, Ph.D. Student, Mathematics, Peking University

  Nov 18, 11:00 - 12:00

  B1 L3 R3426

  In this talk, I will discuss the sharpness conditions for constructing Cʳ conforming finite element spaces from superspline spaces on general simplicial triangulations and introduce the concept of extendability for pre-element spaces, which unifies both superspline and finite element spaces under a common framework.

Nov 9 - Nov 15, 2025

• 

  First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

  Arto Maranjyan, Ph.D. Student, Computer Science

  Nov 13, 12:00 - 13:00

  B9 L2 R2325

  machine learning optimization asynchronous algorithms Training

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

  Mathematical Modeling of Two-Population Pedestrian Congestion

  Noureddine Igbida, Full Professor, Applied Mathematics, Institut de Recherche XLIM, Université de Limoges

  Nov 11, 16:00 - 17:00

  B1 L3 R3426

  Diffusion Models pedestrian dynamics numerical methods

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

  Reduced Krylov Basis Methods for Parametric Partial Differential Equations

  Ludmil Zikatanov, Professor, Mathematics, Pennsylvania State University

  Nov 11, 14:30 - 15:30

  B1 L3 R3119

  This talk presents a user-friendly reduced basis method for solving a family of parametric PDEs by preconditioned Krylov subspace methods including the CG method, GMRes, and BiCGStab.
• 

  Success and Challenges of Computational Fluid Dynamics for Engineering Applications

  Feng Liu, Professor, Department of Mechanical and Aerospace Engineering, University of California, Irvine

  Nov 11, 13:30 - 14:30

  B1 L3 R3119

  CDF Computer simulations Turbulent algorithms

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

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

  Zhihong Xia, Chair Professor, Department of Mathematics, Southern University of Science and Technology

  Nov 11, 10:30 - 11:30

  B9 L3 R3120

  AI machine learning PDEs Dynamical Systems scientific computing

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

  Towards Flexible Polyhedral Nets

  Pirahmad Olimjoni, Ph.D. Student, Applied Mathematics and Computational Science

  Nov 10, 14:30 - 15:30

  B4 L5 R5209

  Isotropic Geometry discrete differential geometry applied mathematics algorithms optimization

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

Nov 2 - Nov 8, 2025

• 

  Proper Random Walk Spline Models

  Eman Kabbas, Ph.D. Student, Applied Mathematics and Computational Science

  Nov 2, 15:00 - 17:00

  B3 L5 R5209

  Bayesian and computational Statistics data science

  This dissertation introduces the Proper Random Walk of order 2 (PRW2), a full-rank Gaussian Markov random field that provides a principled alternative to intrinsic random walk (RW2) priors. By construction, RW2 models exhibit heteroscedastic marginal variances, inflated boundary effects, sensitivity to grid design, and unbounded forecast uncertainty—features that undermine the reliability of inference, particularly in sparse-data settings or beyond the observed domain.

Oct 26 - Nov 1, 2025

• 

  Accelerating Branch-and-Bound Graph Algorithms with GPUs

  Izzat El Hajj, Assistant Professor, Computer Science, American University of Beirut

  Oct 30, 12:00 - 13:00

  B9 L2 R2325

  This talk presents multiple techniques that we have developed to load balance the search tree traversal on GPUs and mitigate the strain on memory capacity and bandwidth.
• 

  Unlocking Euclidean Problems with Isotropic Initialization

  Mikhail Skopenkov, Research Scientist, Computer Science

  Oct 30, 12:00 - 13:00

  B9 L2 R2325

  The seminar introduces a novel, general approach for solving challenging constraint systems in Euclidean geometry problems by leveraging analogous, structure-preserving simplifications found in isotropic geometry to initialize and guide optimization algorithms.
• 

  Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity

  Arto Maranjyan, Ph.D. Student, Computer Science

  Oct 28, 14:30 - 15:30

  B1 L3 R3119

  This talk introduces Ringleader ASGD, the first asynchronous SGD algorithm that attains the theoretical lower bounds for parallel first-order stochastic methods in the smooth nonconvex regime, thereby achieving optimal time complexity under data heterogeneity and without restrictive similarity assumptions.
• 

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

  Toru Ohira, Professor, Dean of the Graduate School of Mathematics, Nagoya University, Japan

  Oct 26, 14:30 - 15:30

  B5 L5 R5220

  Mathematical modeling

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