Events

Dec 7 - Dec 13, 2025

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  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

  During the early 2000s, users of fractional-N frequency synthesizers noticed intermittent pulsing of the noise floor and unexplained spurious frequency components moving about the spectrum for some settings of the fraction. These became known by a number of names including moving spurs, walking spurs, marching spurs, and wandering spurs. The Nonlinear Circuits Group spent ten years finding the root cause of these strange spurs, developing solutions to mitigate them, and transferring the technology into commercial products. This is the story of that journey from a diversity of experimental

Nov 30 - Dec 6, 2025

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  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

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  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. Student, 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

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  Geospatial Data Science for Public Health Surveillance

  Paula Moraga, Assistant Professor, Statistics

  Nov 20, 11:00 - 12:00

  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

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  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.
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  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.
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  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

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  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

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  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.
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  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.

Oct 19 - Oct 25, 2025

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  A Generalized Gaussian Min–Max Framework Inferring Post-Processing Effects in High-Dimensional Estimation

  Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering

  Oct 21, 14:30 - 15:30

  B1 L3 R3119

  statistical analysis

  This talk introduces the Convex Gaussian Min–Max Theorem (CGMT) as a principled framework to analyze the performance of such solutions, and presents a new Gordon-type inequality to study functionals of the solutions that arise when direct deployment is infeasible.

Oct 12 - Oct 18, 2025

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  Bias-Reduced Estimation of Structural Equations Models

  Haziq Jamil, Research Specialist, Statistics

  Oct 16, 12:00 - 13:00

  B9 L2 R2325

  This talk demonstrates that the reduced-bias M-estimation (RBM) framework is a computationally efficient and robust method for mitigating finite-sample bias in structural equation models, outperforming standard estimators, especially in small-sample contexts.

Oct 5 - Oct 11, 2025

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  Block Low-Rank Matrices for Modern Scientific Computing

  Daria Sushnikova, Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering

  Oct 9, 12:00 - 13:00

  B9 L2 R2325

  Block low-rank matrices provide framework for compressing and accelerating large-scale computations. In this talk, I will introduce the basic principles behind these matrix formats, highlight notable algorithms that exploit their structure, and discuss their growing role in modern computational mathematics. Applications ranging from seismic imaging and computational biology to artificial intelligence will be used to illustrate the broad impact of block low-rank methods on science and engineering.

Sep 28 - Oct 4, 2025

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  AI for Science: AI-Driven Knowledge Discovery

  Dongxiao Zhang, Chair Professor, Faculty of Engineering; Provost and Executive Vice President, Eastern Institute of Technology, Ningbo (EIT)

  Oct 2, 14:30 - 15:30

  B1 L3 R3119

  AI machine learning

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

  Efficient Bayesian Methods for Biostatistics

  Janet van Niekerk, Research Scientist, Statistics

  Oct 2, 12:00 - 13:00

  B9 L2 R2325

  bayesian methods

  In this talk, I will present some case studies where we approach near real-time inference for complex Biostatistics models, such as disease mapping and brain activation mapping models, among others often encountered in the biostatistics domain, using INLA.
• 

  Towards Finite Element Tensor Calculus

  Kaibo Hu, Associate Professor, Mathematical Institute, University of Oxford

  Sep 30, 14:30 - 15:30

  B1 L3 R3119

  Finite elements tensor calculus PDEs

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

  New Challenges in the Kinetic Theory of Complex Fluids

  Umberto Zerbinati, Stipendiary Lecturer, Oriel College, University of Oxford

  Sep 29, 16:00 - 17:00

  B1 L4 R4214

  PDEs numerical PDEs Kinetic Theory

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

Sep 21 - Sep 27, 2025

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  Nonparametric Functional Quantiles, Skewness, and Probability Bands via the Functional Signed Directionality

  Emmanuel Ambriz, Postdoctoral Research Fellow, Statistics

  Sep 25, 12:00 - 13:00

  B9 L2 R2325

  functional signed directionality

  We propose the Functional Signed Directionality (FuSD), a nonparametric order for function-valued random elements that defines a pushforward distribution on the real line. This construction enables probabilistic inference in the functional domain: we define set-valued functional quantiles, introduce quantile-based summaries of functional spread and skewness, and construct tight probability bands that reflect the underlying functional law via the FuSD distribution.

Sep 14 - Sep 20, 2025

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  Neural Methods for Amortized Inference with Models for Spatial Extremes

  Raphaël Huser, Associate Professor, Statistics

  Sep 18, 12:00 - 13:00

  B9, L2, R2325

  Neural Bayes estimators are neural networks that approximate Bayes estimators. Once trained, these estimators are not only statistically efficient, but also extremely fast to evaluate and amenable to rapid uncertainty quantification. Neural Bayes estimators thus have compelling advantages when used with spatial models that have a computationally intractable likelihood function, as often the case when modeling spatial extremes. In this talk, I will showcase the power of neural Bayes estimators for spatial extremes in a range of climate-related and geo-environmental data applications.
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  From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

  Peter Richtarik, Professor, Computer Science

  Sep 16, 16:00 - 17:00

  B1 L3 R3119

  AI machine learning optimization algorithms LLM

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

Sep 7 - Sep 13, 2025

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  Gaussian Random Fields on Metric Graphs

  David Bolin, Professor, Statistics

  Sep 11, 12:00 - 13:00

  B9 B2 L2325

  Gaussian random fields Metric graphs Statistical Modeling

  This talk presents a comprehensive mathematical and statistical theory, along with user-friendly software, for modeling data with Gaussian random fields on metric graphs by developing valid covariance functions based on network distance.

Aug 31 - Sep 6, 2025

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

  Peter Richtarik, Professor, Computer Science

  Sep 4, 12:00 - 13:00

  B9 L2 R2325

  AI machine learning optimization algorithms LLM

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

Jul 13 - Jul 19, 2025

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

  Xiaofeng Xu, Ph.D. Student, Applied Mathematics and Computational Science

  Jul 15, 17:00 - 19:00

  B4 L5 R5220

  PDEs optimization machine learning randomized orthogonal greedy algorithm

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

Jul 6 - Jul 12, 2025

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  Discovery of Low-Dimensional Generative Models for Complex Dynamical Systems

  Juan Pablo Muñoz Díaz, Ph.D. Student, Applied Mathematics and Computational Science

  Jul 9, 09:00 - 10:00

  B2 L5 R5220; Zoom Meeting 95274807609

  generative ai applied mathematics bioscience Dynamical Systems deep learning

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

  Najwa Alshehri, Ph.D. Student, Applied Mathematics and Computational Science

  Jul 8, 15:00 - 17:00

  B2/B3 L0 R0215

  finite element method numerical analysis fluid-structure interactions mixed finite elements

  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.

May 18 - May 24, 2025

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  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

  May 18, 09:00 - May 25, 15:30

  Auditorium 0215 between B2 & B3

  stochastic algorithm statistical learning machine learning

  Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.

May 11 - May 17, 2025

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  Perturbation Methods in PDE: from Heuristics to Breakthroughs in Regularity Theory - Part 3

  Dr. Eduardo Teixeira, Professor of Mathematics, Department of Mathematics, University of Central Florida, USA

  May 15, 16:15 - 17:30

  B1 L4 R4102

  This mini-course presents a powerful perturbative framework tailored to tackle critical regularity issues in nonlinear diffusion PDE.
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  The Signed Translation Transformed Depth: Order, Quantiles, Spread, Skewness, and Quantile Regression for Multivariate Functional Data

  Emmanuel Ambriz, Postdoctoral Research Fellow, Statistics

  May 15, 12:00 - 13:00

  B9 L2 R2325

  multivariate analysis

  This seminar introduces the Signed Translation transformed Depth (STtD), a novel interpretable ordering method for multivariate functional data, which enables enhanced distributional analysis, the definition of descriptive tools, and a flexible vine copula-based quantile regression framework.
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  Perturbation Methods in PDE: from Heuristics to Breakthroughs in Regularity Theory - Part 2

  Dr. Eduardo Teixeira, Professor of Mathematics, Department of Mathematics, University of Central Florida, USA

  May 14, 16:15 - 17:30

  B1 L3 R3119

  This mini-course presents a powerful perturbative framework tailored to tackle critical regularity issues in nonlinear diffusion PDE.
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  Nonlocal Degenerate PDEs: Bridging Free Boundary Problems and Critical-point Models

  Eduardo Teixeira, Graduate Director & Professor, Department of Mathematics, University of Central Florida

  May 13, 16:00 - 17:00

  Building 1, Level 3, Room 3426

  nonlocal degeneracies local extrema models regularity estimates

  This talk explores a new class of PDEs featuring nonlocal degeneracies. Our framework unifies two classical scenarios — free boundary problems and critical-point degenerate PDEs; both are recast as local extrema models in our formulation.
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  Topological Phenomena in Artificial Materials

  Xiujuan Zhang, Associate Professor, School of Engineering and Applied Sciences, Department of Material Science and Engineering, Nanjing University

  May 12, 10:00 - 11:00

  B1 L3 R3119

  topological phenomena artificial materials

  This talk will cover the design and realization of novel topological states and effects in acoustic artificial materials, focusing on higher-order topological states, non-Hermitian physics, and acoustic spin- and orbital angular momentum-related topological phenomena.
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  Perturbation Methods in PDE: from Heuristics to Breakthroughs in Regularity Theory - Part 1

  Dr. Eduardo Teixeira, Professor of Mathematics, Department of Mathematics, University of Central Florida, USA

  May 11, 16:15 - 17:30

  B1 L3 R3119

  This mini-course presents a powerful perturbative framework tailored to tackle critical regularity issues in nonlinear diffusion PDE.

May 4 - May 10, 2025

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

  Qilong Pan, Ph.D. Student, Statistics

  May 8, 12:00 - 13:00

  B9 L2 R2325

  machine learning Geospatial Data GPU Computing

  This seminar introduces GPU-accelerated Vecchia approximations to overcome Gaussian Process computational limits, enabling scalable applications for large geospatial datasets and high-dimensional computer model emulations.
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  Current and Future Challenges and Solutions in AI & HPC System and Thermal Management

  Dr. Gamal Refai-Ahmed, Senior Fellow & Chief Architect, AMD Member of U.S. National Academy of Engineering Life Fellow, Canadian Academy of Engineering Fellow, Engineering Institute of Canada Fellow & Distinguished Lecturer, IEEE Life Fellow, ASME

  May 6, 13:00 - 17:00

  B4 L5 R5209

  Led by expert Dr. Gamal Refai Ahmed, this course explores innovative thermal management and packaging solutions for AI and HPC systems, addressing current and future challenges with cutting-edge techniques and next-generation design principles.
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  First-Order Mean-Field Games with Entry-Exit Flow Constraints and Contact-Set Conditions

  AbdulRahman Alharbi, Ph.D. Student, Applied Mathematics and Computational Science

  May 4, 12:30 - 14:30

  B9 L4 R4225; Zoom Meeting 95071981979

  mean-field models free boundary problems

  This dissertation develops and rigorously analyzes first-order mean-field game models incorporating novel mixed boundary conditions to realistically represent population dynamics in bounded domains by eliminating unrealistic entry phenomena that are typically induced by standard Dirichlet conditions.

Apr 27 - May 3, 2025

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  Retraction Maps, Feedback Linearization and Nonholonomic Integrators

  Ravi Banavar, Professor, Systems and Control Engineering, IIT Bombay

  May 1, 12:00 - 13:00

  B9 L2 R2325

  This talk will introduce the utility of retraction maps on Riemannian manifolds to two applications in applied mechanics and control.

Apr 20 - Apr 26, 2025

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  Linear Solvers for Large-Scale Bayesian Modeling

  Lisa Gaedke-Merzhäuser, Postdoctoral Research Fellow, Statistics

  Apr 24, 12:00 - 13:00

  B9 L2 R2325

  In this talk we explore what it means to perform Bayesian inference and introduce the methodology of integrated nested Laplace approximations (INLA).