Events

Mar 21 - Mar 15, 2026

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  Model Predictive Control and Imitation Learning Algorithms for Robot Motion Planning in Physical Human-Robot Interaction

  Aigerim Nurbayeva, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Mar 15, 12:00 - 13:00

  B9 R2325

  Model Predictive Control robotics deep neural networks Numerical Optimization universal robot human-robot interactions NMPC

  This seminar presents a framework for safe and efficient human-robot workspace sharing by using Deep Neural Networks (DNN) and safety filters to rapidly imitate computationally heavy Nonlinear Model Predictive Control method (NMPC), with successful experimental validation on a UR5 manipulator.

Mar 14 - Mar 8, 2026

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  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.
• 

  Provable and Measurable Machine Unlearning in Modern Learning Systems

  Cheng-Long Wang, Ph.D. Student, Computer Science

  Mar 10, 10:30 - 12:30

  B2 L5 R5209

  Machine Unlearning Data Privacy Trustworthy AI Federated learning machine learning AI

  This dissertation examines the foundations of machine unlearning under realistic learning system constraints and proposes both theoretically grounded unlearning algorithms and principled evaluation frameworks for modern learning systems.
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  Learning to Identify and Exploit Neural Network Dynamics in Multi-Step Inference

  Haozhe Liu, Ph.D. Student, Computer Science

  Mar 9, 13:30 - 15:30

  B4 L5 R5220; Zoom Meeting 95866424218

  This dissertation studies the temporal dynamics of multi-step inference and reveals that contributions across steps are sparse and uneven.
• 

  Extreme Computing Universals

  David Keyes, Professor, Applied Mathematics and Computational Science

  Mar 9, 12:00 - 13:00

  B9 L2 R2325

  HPC APIs extreme computing smart systems parallel computing software development

  This talk redefines "extreme" computing as operating under severe resource constraints rather than just massive scale, outlining universal algorithmic, hardware, and system-level strategies to overcome these challenges, illustrated by KAUST success stories.
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  Geospatial Data Science for Public Health Surveillance

  Paula Moraga, Assistant Professor, Statistics

  Mar 9, 11:15 - 12:45

  B4/5 L0 A0215; Zoom Meeting 94713495879

  Geospatial Data Public Health Public health surveillance geospatial statistics statistical methods

  This talk presents an overview of our research on innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and how this work has directly informed strategic policy to reduce disease burden.
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  Simulation of Metasurfaces Described by Generalized Sheet Transition Conditions Using Integral Equations

  Sebastian Celis Sierra, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Mar 8, 00:00 - 01:00

  B9 L2 R2325

  Metasurfaces computational electromagnetics Computer simulations numerical integration

  This seminar outlines the development of computationally efficient integral equation solvers that simulate complex multiscale metasurfaces by modeling their physical geometries as infinitesimally thin sheets governed by generalized sheet transition conditions, thereby avoiding the need for full volumetric discretization.

Mar 7 - Mar 1, 2026

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  Event Status: Cancelled | 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.
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  Assessing Network Middlebox Impact on End-to-End Protocol Behavior via a Distributed and Reprogrammable Framework

  Ilies Benhabbour, Ph.D. Student, Computer Science

  Mar 4, 13:00 - 16:00

  B5 L5 R5220

  cybersecurity Cryptography distributed computing

  This dissertation focuses on the detection and verification of network middleboxes thanks to the creation of a new distributed framework called NoPASARAN.
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  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.
• 

  Bayesian Inference for Partially Observed Continuous-Time Processes

  Amin Wu, Ph.D., Statistics

  Mar 3, 10:00 - 12:00

  B5 L5 R5220

  McKean-Vlasov SDEs bayesian inference markov chains Monte Carlo

  This thesis develops Bayesian inference methods for partially observed stochastic differential equations (SDEs) with unknown parameters, focusing on the stochastic Volterra equation (SVE), non-synchronous diffusions, and McKean-Vlasov SDEs. Employing Euler-Maruyama discretization.
• 

  From Prompts to Production: The Systems Agenda for Agentic AI

  Marco Canini, Professor, Computer Science

  Mar 2, 12:00 - 13:00

  B9 L2 R2325

  AI observability reproducibility Computer Information Systems

  In this talk, I will discuss our recent work on benchmarking, evaluation, and deployment of multi-agent LLM systems, and use it to outline a broader research agenda for agentic AI as a systems discipline - where progress depends not only on better models, but on principled infrastructure for observability, reproducibility, safe experimentation, and scalable execution.
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  From Edge to Intelligence: Light-Driven Synaptic Devices for Adaptive Healthcare and Vision Systems

  Dayanand Kumar, Postdoctoral Research Fellow, Electrical and Computer Engineering

  Mar 1, 12:00 - 13:00

  B9 L2 R2325

  optoelectronic devices light-driven synaptic devices neuromorphic electronics edge computing

  This talk presents a flexible, back-end-of-line compatible optoelectronic synapse developed for neuromorphic edge computing.

Feb 28 - Feb 22, 2026

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  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.
• 

  Towards Scalable and Structured Understanding in Visual LLMs

  Mohamed Elhoseiny, Associate Professor, Computer Science

  Feb 23, 12:00 - 13:00

  B9 L2 R2325

  LLM Visual Language Models VLMs visual computing

  In this talk, we explore a suite of recent advances toward scalable, structured video comprehension using Large Vision Language Models (Video LLMs).
• 

  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 21 - Feb 15, 2026

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  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.
• 

  Eyes in the Sky: AI-Based Camel Identification and Tracking Using Drones

  Basem Shihada, Program Chair, Computer Science

  Feb 16, 12:00 - 13:00

  B9 L2 R2325

  AI drones animal tracking

  In this talk, I present a low-cost, AI-powered drone system capable of recognizing and tracking camels from the air.
• 

  Tapping Into the Full Potential of the Stratosphere

  Mohamed-Slim Alouini, Al-Khawarzmi Distinguished Professor, Electrical and Computer Engineering

  Feb 15, 12:00 - 13:00

  B9 L2 R2325

  HAPSs Large-scale Connectivity communication technology FSO systems Free-space optical communications optical connectivity

  This talk examines how High-Altitude Platform Stations (HAPS) leverage intelligent beam management and optical links to democratize broadband access and provide resilient solutions for disaster recovery.

Feb 14 - Feb 8, 2026

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  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.
• 

  Accelerating Branch-and-Bound Graph Algorithms with GPUs

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

  Feb 9, 12:00 - 13:00

  B9 L2 R2325

  parallel computing optimization GPU Algorithms HPC Graph Theory Efficient

  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.
• 

  Rising Stars in AI Symposium 2026

  Feb 9, 08:00 - 17:00

  KAUST Campus

  AI artificial intelligence

  Join the AI research community for a multi-day symposium focused on emerging research directions and collaboration.
• 

  AI for Optics and Optics for AI

  Wolfgang Heidrich, Professor, Computer Science

  Feb 8, 12:00 - 13:00

  B9 L2 R2325

  This talk will highlight recent work on the interconnection between AI techniques and (imaging) optics.

Feb 7 - Feb 1, 2026

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  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.
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  Efficient Machine Learning for Scientific and Medical Applications

  Yasir Ghunaim, Ph.D., Computer Science

  Feb 4, 18:00 - 20:00

  B4/5 L0 A0215

  Efficient Machine Learning machine learning Graph Neural Networks

  This dissertation addresses key challenges of machine learning in scientific and medical domains by developing methods that improve model efficiency, data efficiency, and learning under real-world constraints.
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  From Dialects to Peptides: Scalable and Efficient AI for People

  Muhammad Abdul-Mageed, Canada Research Chair, Natural Language Processing and Machine Learning; Associate Professor, School of Information, Department of Linguistics, The University of British Columbia

  Feb 2, 12:00 - 13:00

  B9 L2 R2325

  AI Efficient Efficient Machine Learning

  This talk presents a unified AI framework for decoding complex human and biological signals - spanning African and Arabic dialects to proteomics - by prioritizing rigorous measurement, cultural competence, and computational efficiency to ensure global scalability and accessibility.
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  Uncertainty-Aware Learning: From Bayesian Neural Networks to Agentic Decision Making

  Theodore Papamarkou, Founder, PolyShape; Visiting Professor, School of Applied Mathematical and Physical Sciences (SEMFE), National Technical University of Athens (NTUA)

  Feb 1, 12:00 - 13:00

  B4/5 L0 A0215

  uncertainty quantification neural network Bayesian modeling AI

  This talk points out that uncertainty quantification is important for reliable AI, and that modern machine learning should be viewed through the lens of probabilistic decision making.
• 

  Uncertainty Quantification with Conformal Prediction in Energy Data

  Tarek AlSkaif, Associate Professor, Energy Informatics, Wageningen University (WUR)

  Feb 1, 12:00 - 13:00

  B9 L2 R2325

  conformal prediction machine learning uncertainty quantification

  The talk will introduce the fundamentals of conformal prediction (CP) - a flexible, model-agnostic uncertainty quantification framework for generating statistically valid uncertainty estimates in energy applications - and demonstrate how it can be layered on top of machine learning models to produce reliable prediction intervals.

Jan 31 - Jan 25, 2026

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  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.
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  Beyond Bayesian Uncertainty: a Variational Perspective

  Chérief-Abdellatif Badr-Eddine, CNRS (Chargé de Recherche) Researcher, Laboratoire de Probabilités, Statistique et Modélisation (LPSM), Sorbonne Université, France

  Jan 28, 12:00 - 13:00

  B4/5 L0 A0215

  In this talk, I will explore how the statistics and machine learning communities are expanding the frontiers of uncertainty quantification beyond traditional Bayesian frameworks.
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  Thoughts About Machine Learning

  Jürgen Schmidhuber, Professor, Computer Science

  Jan 26, 15:30 - 17:30

  B9, Lecture Hall 1, R-2322

  AI machine learning deep learning

  A weekly seminar series from January 26 to April 20, 2026, exploring advanced AI concepts beyond the scope of standard deep learning courses.
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  Shedding New Light on the Past: Applications of Multi-Light Image Collections in Cultural Heritage

  Ruggero Pintus, Senior Researcher, Center for Advanced Studies, Research and Development in Sardinia (CRS4)

  Jan 26, 12:00 - 13:00

  B9 L2 R2325

  interactive visualization image processing data acquisition

  This talk provides an overview of developing advanced tools for the digitization and exploration of Cultural Heritage assets, with a deep dive into Multi-Light Image Collections (MLICs) at Visual and Data Intensive Computing group at CRS4 (Italy).
• 

  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.
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  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.
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  Ultrawide Bandgap Nano Devices

  Glen Isaac Maciel García, Ph.D. Student, Applied Physics

  Jan 25, 12:00 - 13:00

  B9 L2 R2325

  Ultrawide bandgap materials nano-engineering

  This seminar explores how nanoscale device architectures can unlock new functionalities in ultrawide bandgap (UWBG) materials beyond conventional applications.

Jan 24 - Jan 18, 2026

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  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 17 - Jan 11, 2026

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  Causal Representation Learning

  Ali Tajer, Professor, Electrical, Computer, and Systems Engineering

  Jan 15, 15:00 - 16:00

  B1 L2 2202

  machine learning causal representation learning statistical learning Information theory

  In this talk, we will explore the latest advancements in the emerging field of causal representation learning (CRL).
• 

  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.
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  From Images to Neural Scenes: Efficient Rendering, Robust Editing, and Human-Aware Reconstruction

  Sara Rojas Martinez, Ph.D., Electrical and Computer Engineering

  Jan 12, 04:00 - 06:00

  Zoom Meeting ID 97415296117

  machine learning 3D reconstruction visual computing

  This thesis addresses these challenges by introducing efficient mesh-based distillation for real-time rendering, view-consistent text-guided scene editing, and human-aware reconstruction that improves geometry and camera estimation.

Dec 13 - Dec 7, 2025

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  Overcoming Catastrophic Forgetting: From Efficiency to Safety

  Lama Alssum, Ph.D. Student, Computer Science

  Dec 11, 14:00 - 15:00

  B3 L5 R5220

  AI machine learning Computer Vision

  This thesis addresses the challenge of catastrophic forgetting in AI by developing novel continual learning methods that enhance memory efficiency in video analysis and preserve safety alignment in large language models, ensuring reliable adaptation in both resource-constrained and safety-critical applications.
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  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.
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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.
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  Safety-Aware Pre-Flight Trajectory Planning for Urban and Rural UAVs Under Mechanical and GPS Failure Scenarios

  Amin Almozel, Ph.D. Student, Electrical and Computer Engineering

  Dec 8, 12:30 - 14:30

  Building 4, Level 5, Room 5209

  safety planning uav failures trajectory design

  This work develops a safety-aware pre-flight planning framework for UAV missions, addressing risks such as propulsion failure, GPS loss, and communication outages. It ensures drones remain within reach of safe landing sites and accounts for dense urban constraints, including no-fly zones. Using a high-fidelity simulator with real geospatial data, the framework is validated for both routine and emergency scenarios, offering a structured approach to reliable, failure-tolerant UAV trajectory planning.