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 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.
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.
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.
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.
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.
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.
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.
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.
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.
Daria Sushnikova, Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Xiujuan Zhang, Associate Professor, School of Engineering and Applied Sciences, Department of Material Science and Engineering, Nanjing University
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.
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.
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.
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.
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.
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).
Homogenization and Wave Functional Materials Ying Wu, Program Chair, Applied Mathematics and Computational Science Apr 17, 12:00 - 13:00 B9 L2 R2325 Wave functional materials Homogenization In this talk, I will give a brief review on the progress from on homogenization and its impact on achieving unusual wave functional materials, such as zero-index materials in which the effective refractive index is vanishing.
The Earliest Arrival of Quantum Advantage David Keyes, Professor, Applied Mathematics and Computational Science Apr 10, 12:00 - 13:00 B9 L2 R2325 quantum computing quantum processing unit supercomputers This talk outlines a "quantum first" strategy for future supercomputers, integrating QPUs, GPUs, and CPUs to optimize energy efficiency and accelerate scientific computing while addressing the current challenges and projecting the maturation of quantum computing by leveraging classical supercomputing advancements.
Helmholtz–Korteweg Equations: Modeling, Analysis, and Applications Umberto Zerbinati Apr 8, 15:00 - 16:00 B1 L4 R4102 Korteweg fluids Helmholtz–Korteweg equation This talk presents a derivation and analysis of Helmholtz-Korteweg equations, extended to nematic-Korteweg fluids, including well-posedness proofs, numerical methods, and predictions for wave propagation and scattering phenomena with experimental implications.
Workshop on Analysis and PDEs Apr 7, 08:00 - Apr 10, 17:00 B3, L5, R5220 PDEs Analysis of PDE's fluid dynamics free boundary problems energy minimization Mathematical Biology Join us for the Workshop on PDEs to explore advances, challenges and methods in the analysis and computation of partial differential equations.
Some Contributions to Particle and Unbiased Simulation Methods Elsiddig Awadelkarim Elsiddig, Postdoctoral Research Fellow, Statistics Mar 26, 10:00 - 12:00 B3 L5 R5209; Zoom Meeting 95303621356 Bayesian Statistics machine learning This thesis develops unbiased estimators and efficient particle filters, leveraging Feynman-Kac formulae and multilevel techniques, to address challenges in parameter estimation, filtering, invariant measure approximation, and stationary distribution computation for partially observed diffusion processes and McKean-Vlasov stochastic differential equations.
Dimension Liftings for Quantum Computation of Partial Differential Equations and Related Problems Shi Jin, Director of Institute of Natural Sciences, Chair Professor of Mathematics, Shanghai Jiao Tong University Mar 25, 16:00 - 17:00 B1 L3 R3119 PDEs Schrödinger equation quantum simulation quantum computing This lecture presents a systematic framework for developing quantum simulation algorithms for general differential equations, using dimension lifting to transform nonlinear and time-dependent problems into forms solvable by simulating the Schrödinger equation.
Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining Mikhail Moshkov, Professor, Applied Mathematics and Computational Science Mar 20, 12:00 - 13:00 B9, L2, R2325 Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.
On the Use of "Conventional" Unconstrained Minimization Solvers for Training Regression Problems in Scientific Machine Learning Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures Mar 13, 12:00 - 13:00 B9 L2 R2325 petsc PETScML machine learning This talk introduces PETScML, a framework leveraging traditional second-order optimization solvers for use within scientific machine learning, demonstrating improved generalization capabilities over gradient-based methods routinely adopted in deep learning.
