Events

Nov 30 - Dec 6, 2025

• 

  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.
• 

  I/O Coordination for Better Resource Sharing - From HPC to AI Storage

  Xiaosong Ma, Department Chair and Professor of Computer Science, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

  Dec 1, 12:00 - 13:00

  B9, Level 2, Room 2325

  ai storage

  In this talk, through a personal journey of parallel and distributed storage systems, I hope to share observations and lessons from these past projects.
• 

  Probabilistic Modelling and Analysis of Power System Dynamics and Stability

  Kazi N. Hasan, Senior Lecture, School of Engineering, RMIT University, Australia

  Nov 30, 12:00 - 13:00

  B9 L2 R2325

  power grid

  This presentation will demonstrate the most widely used probabilistic methods applied to power system stability analysis with their specific applications in frequency stability, small-disturbance stability, transient stability, and voltage stability.

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.
• 

  Balancing Accuracy and Efficiency: Compact Representations for Flow and Multivariate Visualization

  Amani Ageeli, Ph.D. Student, Computer Science

  Nov 25, 10:00 - 12:00

  B3 L5 R5220

  Scientific Visualization real-time rendering computer graphics interactive visualization large datasets multivariate functional data

  This thesis addresses the challenge of interactively visualizing massive scientific datasets by introducing novel frameworks that strategically balance accuracy and efficiency for scalable multivariate filtering, objective time-dependent flow analysis, and hybrid, complexity-guided flow reconstruction.
• 

  Trustworthy Learning for Multichannel Biosignals: Calibrated Uncertainty, Long-Range State-Space Modeling, and Inverse-Rate Regularization

  Jiahao Hu, Ph.D. Student, Electrical and Computer Engineering

  Nov 24, 16:00 - 17:30

  B3 L5 R5220; Zoom Meeting 91006648759

  This thesis advances electrophysiological signal analysis by delivering calibrated probabilities, efficient long-range multichannel modeling, and near-optimal regularization, establishing a practical, principled framework for trustworthy biosignal understanding.
• 

  Training Neural Networks at Any Scale

  Volkan Cevher, Associate Professor, School of Engineering, Swiss Federal Institute of Technology (EPFL), Switzerland

  Nov 24, 12:00 - 13:00

  B9 L2 R2325

  machine learning Numerical simulation and analysis Reinforcement Learning deep learning optimization

  The talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning, spanning domains from vision to language models.
• 

  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.
• 

  Robust Locomotion of Legged Robots with Closed-Loop Guarantees

  Mohamed Elobaid, Research Scientist, Electrical and Computer Engineering

  Nov 23, 12:00 - 13:00

  B9 L2 R2325

  This talk presents a predictive control framework allowing general legged robots to walk robustly, and describes its validation results on both humanoids and quadrupeds.

Nov 16 - Nov 22, 2025

• 

  The TetraheDrone: Motorizing Alexander Graham Bell's Kites

  Bilal Maassarani, Ph.D. Candidate, Electrical and Computer Engineering

  Nov 20, 15:00 - 16:00

  B5 L5 R5209; Zoom Meeting 93320066680

  This thesis introduces a scalable and robust modular VTOL system (TetraheDrone), validated through flight experiments, that leverages a recursive tetrahedral architecture and a unified control methodology to achieve stable, efficient hybrid flight for single and multi-module assemblages.
• 

  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.
• 

  Observer-Relative Flow Visualization and Objective Feature Extraction

  Xingdi Zhang, Ph.D. Student, Computer Science

  Nov 19, 16:30 - 18:00

  B1 L4 R4214

  visual computing scientific computing deep learning

  This dissertation develops an integrated toolkit of novel visualization and feature extraction methods, grounded in a Riemannian geometry framework, to enable an objective, observer-relative, and physically consistent analysis of complex unsteady flows.
• 

  Modeling, Analysis, and Control for Planning and Operation of Modern Mixed-Generation Power Systems

  Otavio Bertozzi, Ph.D. Student, Electrical and Computer Engineering

  Nov 19, 09:30 - 10:30

  B5, L5, R5209

  mixed-generation power systems stability-aware modeling data-driven planning and operation renewable energy integration

  This dissertation presents an interpretable framework for modeling, planning, and control of mixed-generation power systems, integrating ternary stability-aware planning, reinforcement learning-based control tuning, and data-driven stability forecasting to bridge analytical models with real-time operation in converter-dominated grids.
• 

  Event Status: Cancelled | 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.
• 

  Gaussian Splatting: A Novel Paradigm for 3D Scene Representation and Rendering

  Ivan Viola, Professor, Computer Science

  Nov 17, 12:00 - 13:00

  B9 L2 R2325

  This talk will provide a comprehensive overview of 3D Gaussian Splatting, a novel and powerful technique for 3D scene representation and rendering.
• 

  Aligning the “Socio” in Socio-Technical Control: Trustworthy, Fair, and Efficient Resource Allocation with Karma Economies

  Ezzat Elokda, Postdoctoral Researcher, Information Technology and Electrical Engineering, ETH Zurich

  Nov 16, 12:00 - 13:00

  B9 L2 R2325

  This talk introduces karma economies, a novel class of non-monetary mechanisms that leverage the repeated and dynamic nature of many socio-technical resources to allocate them in a trustworthy, fair, and efficient manner.
• 

  High Specificity Microwave Wearable Biosensors for Remote Health Monitoring

  Firas Fatani, Ph.D. Student, Electrical and Computer Engineering

  Nov 16, 08:00 - 10:00

  B2/3 L0 R0215

  Antenna Integration Microwave circuits Biosensors wearable technology Flexible and Disposable Wireless Sensors additive manufacturing electromagnetics

  This dissertation investigates an alternative route based on electromagnetic (EM) sensing to realize passive, label-free, and cost-effective wearable biosensors, addressing key limitations of existing technologies.

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.
• 

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

  Qilong Pan, Ph.D. Student, Statistics

  Nov 12, 09:00 - 11:00

  B5 L5 R5209

  statistics spatio-temporal statistics GPU Computing HPC

  This thesis advances the computational efficiency of Vecchia approximation methods for Gaussian Processes (GPs), emphasizing GPU-based implementations for large-scale geospatial analysis and computer emulation. Traditional GPs require expensive covariance matrix inversions, which this work overcomes using scalable Vecchia-based approximations without sacrificing accuracy.
• 

  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.
• 

  A Service-Based Approach to Drone Service Delivery in Skyway Networks

  Athman Bouguettaya, Professor, School of Computer Science, The University of Sydney

  Nov 10, 12:00 - 13:00

  B9 L2 R2325

  drones optimization Quality of service

  This talk presents a novel service framework that optimizes drone package delivery by composing the best services based on payload, time, and cost while considering environmental factors for both single drones and swarms.
• 

  On adopting Gauss maps into geometry tools for Computer-Aided Design

  Victor Ceballos Inza, Ph.D. Student, Computer Science

  Nov 10, 11:30 - 13:00

  B1, L3, R3426

  gauss maps computer-aided design

  We explore the integration of Gauss maps into computational tools for design and fabrication, with a particular focus on architectural applications. The work presented here addresses this task by introducing novel discretisation theories, computational algorithms, and interactive tools that embed Gauss maps at three progressively deeper levels of geometric modelling: as a means to compute and interpret curvature for surface panelling, as an interactive visual tool to guide the design of developable surfaces, and as a modelling domain via isotropic geometry, enabling dual surface manipulation for the controlled roughening of a triangulation.
• 

  AI-Driven Smart Wearables in Health and Sport Application

  Luyao Yang, Ph.D. Student, Networking Research Lab

  Nov 10, 09:00 - 11:00

  B1 L3 R3119

  smart health

  This dissertation presents a systematic review of smart wearable applications in sports and health, analyzing sensors, communication, algorithms, and evaluation schemes.
• 

  6th KAUST 6G Summit

  Nov 10, 00:00 - Nov 12, 17:00

  Virtual

  6G 6G and Beyond

  KAUST's annual summit on emerging 6G technologies and applications.
• 

  Enabling Next Generation Wireless Communication through mm-Wave Reconfigurable Intelligent Surface (RIS)

  Atif Shamim, Program Chair, Electrical and Computer Engineering

  Nov 9, 12:00 - 13:00

  B9, L2, R2325

  Next Generation Wireless Communication Systems 5G and beyond Reconfigurable Intelligent Surfaces

  The mm-wave 5G and beyond communication systems significantly improve the data rate, user capacity, and latency, however, the electromagnetic (EM) wave propagation suffers from high atmospheric attenuation as compared to the sub-6 GHz bands. Therefore, the quality of wireless communication gets severely affected in an environment where multiple obstacles, such as buildings and trees, are present, and thus, communication coverage is typically limited to line of sight (LOS).

Nov 2 - Nov 8, 2025

• 

  Bayesian Inverse Problems: Methods, Tools, and Applications

  Amal Alghamdi, Founder, Impact Alpha, Saudi Arabia

  Nov 4, 14:30 - 15:30

  B1 L3 R3119

  This talk will discuss methods, tools and applications for solving inverse problems.
• 

  Urban Air Mobility Communications: Performance Analysis and Design

  Abdullah Abu Zaid, Ph.D. Student, Electrical and Computer Engineering

  Nov 3, 14:00 - 16:00

  B1 L2 2202

  urban air mobility communications wireless communication

  This dissertation develops a foundation for designing robust and reliable communication systems for urban air mobility (UAM), supporting the anticipated large-scale deployment of UAM in smart cities.
• 

  Don’t be the Jean-Claude Van Damme of Your Research Area

  Ahmed El-Roby, Associate Professor, School of Computer Science, Carleton University

  Nov 3, 12:00 - 13:00

  B9, L2, R2325

  What does the career of an action movie icon have to teach us about surviving and thriving in academic research? In this talk, we’ll explore surprising parallels between cinematic fame and the life cycle of hot research topics, using personal experience from the rise and disruption of question answering over knowledge graphs. Join us for unexpected insights, practical lessons, and a lively discussion on how to avoid fading into irrelevance as the scholarly landscape evolves.
• 

  Computational and Statistical Advances in Spatio-Temporal Modeling: Causality, Deep Learning, and High-Performance Computing

  Zipei Geng, Ph.D. Student, Statistics

  Nov 2, 16:00 - 18:00

  B2, L5, R5209

  spatio-temporal modeling causality and deep learning high-performance computing

  Recent advances in environmental monitoring and remote sensing have led to an unprecedented increase in spatial and spatio-temporal data complexity, presenting both opportunities and challenges for environmental science. This thesis explores three critical challenges in environmental data analysis.
• 

  Performance Analysis for High Altitude Platform Station-Assisted Wireless Communication Networks

  Zhengying Lou, Ph.D. Student, Electrical and Computer Engineering

  Nov 2, 16:00 - 17:30

  B1, L3, R3123

  high altitude platform stations Non-Terrestrial Networks wireless communication networks

  High altitude platform stations (HAPS) have recently emerged as a pivotal stratospheric segment within the broader non-terrestrial network (NTN) ecosystem, offering a promising solution for enhancing coverage, reliability, and efficiency in next-generation wireless networks. This thesis presents a comprehensive investigation into the architecture, performance, and integration of HAPS-assisted wireless communication networks from multiple perspectives.
• 

  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.
• 

  Cybersecurity Threats to Power Grid Operations from the Demand-Side Response Ecosystem

  Subhash Lakshminarayana, Associate Professor, School of Engineering, University of Warwick, UK

  Nov 2, 12:00 - 13:00

  B9, L2, R2325

  This talk will highlight the cybersecurity threats from IoT-enabled energy smart appliances (ESAs) such as smart heat pumps, electric vehicle chargers, etc., to power grid operations.
• 

  The KAUST 2025 Workshop on Statistics

  Nov 2, 09:00 - Nov 6, 17:00

  Between Building 2 / 3, Level 0, Auditorium 0215

  The KAUST Statistics Workshop will feature the latest research on statistical methods and modeling to address real-world challenges in health, environment, climate, energy and beyond.

Oct 26 - Nov 1, 2025

• 

  On Computation and Robustness Issues in Spatial Statistics

  Sihan Chen, Ph.D. Student, Statistics

  Oct 30, 14:30 - 16:30

  B2, L5, R5220

  robust spatial inference computational methods spatial statistics

  This thesis develops and evaluates robust statistical methods for the analysis and modeling of spatial data, with a focus on improving inference reliability in the presence of outliers and computational challenges.
• 

  Event Status: Cancelled | 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.
• 

  BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training

  Laurent Condat, Senior Research Scientist, Computer Science

  Oct 27, 12:00 - 13:00

  B9 L2 R2325

  optimization Distributed algorithms Signal and Image Processing

  We introduce BiCoLoR, the first algorithm to combine local training with bidirectional compression using arbitrary unbiased compressors, achieving accelerated complexity and demonstrating superior empirical performance.
• 

  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.
• 

  Integrated Silicon Photonics with Quantum Dot On-Chip Lasers

  Yating Wan, Assistant Professor, Electrical and Computer Engineering

  Oct 26, 12:00 - 13:00

  B9, L2, R2325

  Integrated silicon photonic has emerged as a leading solution for scalable, power-efficient, and environmentally friendly applications. This talk will focus on the prospects and applications of on-chip lasers, a critical component driving the advancement of photonic integrated circuits (PICs). We will discuss various approaches to integrating lasers on silicon and their applications in optical communication, computing, and LiDAR, with a particular emphasis on heterogeneous integration of quantum dot (QD) lasers. QD lasers offer unique advantages, including high immunity to reflection, superior thermal stability, low threshold, and long-term reliability, making them ideal for high-speed optical interconnects, AI-driven computing, and quantum photonic systems. By leveraging the defect tolerance and temperature resilience of QDs, we achieve high-performance, energy-efficient integration with silicon photonics. This talk will highlight recent breakthroughs in QD-on-silicon integration, performance optimizations, and future directions toward heterogeneous photonic systems, paving the way for a new generation of high-speed, low-energy, and scalable optical circuits for next-generation applications.

Oct 19 - Oct 25, 2025

• 

  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.
• 

  One-on-One Statistical Consulting for KAUST Researchers 2025

  Oct 20, All day

  B1 L4 R4102

  In recognition of World Statistics Day, a United Nations–designated observance that highlights the importance of trustworthy data and sound statistical methods, the KAUST Student Chapter of the American Statistical Association (ASA) is hosting one-on-one consulting sessions for KAUST students and researchers. Graduate students and postdoctoral researchers from the Statistics Program will be available to provide personalized guidance on research-related statistical challenges. These consultations aim to strengthen the use of statistical methods across disciplines, support high-quality research

Oct 12 - Oct 18, 2025

• 

  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.
• 

  Learning-Based Observer Design for Nonlinear Systems with Convergence Guarantees

  Yasmine Marani, Ph.D. Student, Electrical and Computer Engineering

  Oct 14, 10:00 - 12:00

  B3 L5 R5220

  This thesis advances nonlinear estimation by proposing learning-based nonlinear observers with convergence guarantees. The proposed approaches overcome the limitations of the existing methods by being applicable to a wide range of nonlinear systems while providing global convergence guarantees. Furthermore, the developed observers are robust to disturbances, sensor delay, and measurement noise.