Events

May 16 - May 10, 2026

• 

  Differentiable Optics for Automated Optical Design and End-to-end Computational Imaging

  Xinge Yang, Ph.D. Student, Computer Science

  May 11, 17:00 - 18:30

  B1 R2202

  differentiable optics computational imaging optical design automated design augmented reality virtual reality AR VR

  This thesis develops a differentiable optics and image simulation framework, with applications in automated lenses and AR/VR design and end-to-end computational imaging with novel camera systems.

May 9 - May 3, 2026

• 

  Multimodal Agents: From Automation toward Open-Ended Self-Improvement

  Mingchen Zhuge, Ph.D. Student, Computer Science

  May 9, 17:30 - 19:30

  B4 R5220; Zoom Meeting 91489077683

  AI agents coding Multi-agent systems world models recursive self-improvement LLM Deep Reinforcement Learning

  This thesis presents practical methodologies for building scalable multimodal agents that move from narrow automation toward open-ended self-improvement.
• 

  Learning under Limited Information across Federated, Multi-Agent, and LLM Settings

  Salma Kharrat, Ph.D. Student, Computer Science

  May 7, 15:00 - 16:45

  B3 R5220

  Federated learning personalized learning decentralized learning Reinforcement Learning black-box optimization prompt optimization decentralized systems combinatorial optimization observability inference Trustworthy AI trustworthy machine learning intelligent systems LLM

  This dissertation studies learning under structural information constraints across three major paradigms: federated learning, cooperative multi-agent reinforcement learning, and black-box optimization of large language models.
• 

  What Survives When Code Doesn’t?

  Dr. Laurent Bindschaedler, Research Group Leader, Max Planck Institute for Software Systems (MPI-SWS)

  May 4, 12:00 - 13:00

  B9 R2325

  Trustworthy AI trustworthy machine learning coding AI axplainable AI software development

  This talk explores how AI-driven code generation shifts the role of software from a durable artifact to a disposable implementation and argues for a new computational model for agentic software that formalizes the fundamental guarantees of intent, state, composition, and effect into explicit, enforceable contracts.

May 2 - Apr 26, 2026

• 

  Causal Reasoning in Medical Digital Twins: Methods and Architectures

  Sakhaa Alsaedi, Ph.D. Student, Computer Science

  Apr 29, 03:00 - 05:00

  B3 R5209

  explainable AI reasoning causal representation learning medical digital twins personalized medicine AI AI for healthcare

  This dissertation develops a principled computational framework for causal reasoning in Medical digital twins (MDT) systems, moving beyond correlation-driven approaches toward explainable and biologically grounded decision support.
• 

  Publication Ethics and Authorship - Including the Responsible and Ethical Use of AI in Research

  Sidney Engelbrecht, Senior Research Compliance Specialist, Research Operations

  Apr 26, 12:00 - 13:00

  B9 R2325

  research ethics scientific research AI

  This talk outlines essential publication ethics and best practices at KAUST, focusing on authorship criteria, researcher responsibilities, dispute resolution, and the ethical use of AI in scholarly writing.

Apr 25 - Apr 19, 2026

• 

  Introduction to Research Ethics and Integrity - Including Issues Around Research Misconduct

  Sidney Engelbrecht, Senior Research Compliance Specialist, Research Operations

  Apr 19, 12:00 - 13:00

  B9 R2325

  research ethics scientific research

  This talk outlines KAUST's research ethics governance, detailing the submission processes for Research Ethics Committees and the institutional procedures for reporting misconduct and grievances.

Apr 18 - Apr 12, 2026

• 

  Private and Robust Learning for Decision Making

  Yulian Wu, Ph.D. Student, Computer Science

  Apr 14, 15:00 - 17:00

  B4 R5220; Zoom Meeting 98132861421

  decision making Trustworthy AI privacy-preserving AI Data Privacy machine learning artificial intelligence AI

  This thesis develops the theoretical foundations of decision-making under privacy, heavy-tailed feedback, and data contamination, establishing fundamental limits and designing near-optimal algorithms across bandits, reinforcement learning, and RLHF.

Apr 11 - Apr 5, 2026

• 

  The Role of Humans in Scientific Discovery in the Age of LLMs — Beyond Asking: Turning LLMs into Research Collaborators

  Sir Bashir M. Al-Hashimi, Vice President, Research & Innovation, King’s College London (KCL); Distinguished Professor, Department of Engineering, Faculty of Natural, Mathematical & Engineering Sciences, King’s College London (KCL)

  Apr 8, 12:00 - 14:15

  B9 R2325

  AI artificial intelligence LLM scientific research scientific knowledge Assistive Technology

  Rather than offering definitive conclusions, this talk seeks to stimulate dialogue, question assumptions, and inspire new forms of collective thinking about the future of scientific research and doctoral training in an AI-driven world.
• 

  DePIN: From Decentralization Promise to Security Reality - A Critical Dissection of Trust, Privacy, and Architectural Illusions

  Roberto Di Pietro, Professor, Computer Science

  Apr 6, 12:00 - 13:00

  B9 R2325

  privacy preserving techniques cybersecurity Web and Network security DePIN Decentralized Physical Infrastructure Networks VPNs dVPNs computer networking decentralized infrastructure systems

  In this talk, we critically examine DePIN systems through the lens of two complementary studies. First, we provide a systematic analysis of the DePIN paradigm, identifying its core architectural pillars - blockchain, IoT, and tokenomics - and exposing fundamental vulnerabilities arising from operating in a zero-trust, open-participation environment.

Apr 4 - Mar 29, 2026

• 

  Can AI Make Physicians Better at Diagnosis?

  Ihsan Ayyub Qazi, Full Professor, Computer Science, Lahore University of Management Sciences (LUMS)

  Mar 30, 12:00 - 13:00

  B9 R2325

  AI Trustworthy AI Diagnosis behavioral analysis decision making LLM Biomedical

  This talk presents a framework for understanding physician-AI collaboration in clinical decision-making, showing that while structured AI literacy training can significantly improve diagnostic accuracy, physicians remain vulnerable to automation bias when LLMs err, highlighting the need to carefully manage human trust and reasoning in AI-assisted clinical decision-making.

Mar 21 - Mar 15, 2026

• 

  Demystifying Adversarial Patch Attacks and Defenses in the Physical World

  Tao Ni, Assistant Professor, Computer Science

  Mar 16, 12:00 - 13:00

  B9 R2325

  Trustworthy AI Computer Vision computational predictions spoofing

  In this talk, I will introduce a series of adversarial patch attacks in face recognition systems and autonomous driving cars, and present our recent studies in developing a zero-shot and patch-agnostic defense framework.

Mar 14 - Mar 8, 2026

• 

  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.
• 

  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.

Mar 7 - Mar 1, 2026

• 

  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.
• 

  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.

Feb 28 - Feb 22, 2026

• 

  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).

Feb 21 - Feb 15, 2026

• 

  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.

Feb 14 - Feb 8, 2026

• 

  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.

Feb 7 - Feb 1, 2026

• 

  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.
• 

  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.

Jan 31 - Jan 25, 2026

• 

  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.
• 

  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.
• 

  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.

Dec 13 - Dec 7, 2025

• 

  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.
• 

  Tales of a Spur Hunter: The Wandering Spur

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

  Dec 7, 12:00 - 13:00

  B9 L2 R2325

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

Dec 6 - Nov 30, 2025

• 

  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.
• 

  First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data

  Arto Maranjyan, Ph.D. Student, Computer Science

  Dec 4, 08:30 - 11:00

  B4/5 L0 A0215

  machine learning optimization asynchronous algorithms Training

  This thesis introduces a novel framework for asynchronous first-order stochastic optimization centered on heterogeneous worker speeds that remain fast, stable, and even provably optimal.
• 

  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.

Nov 29 - Nov 23, 2025

• 

  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.
• 

  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.

Nov 22 - Nov 16, 2025

• 

  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.
• 

  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.

Nov 15 - Nov 9, 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.
• 

  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.

Nov 8 - Nov 2, 2025

• 

  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.

Nov 1 - Oct 26, 2025

• 

  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.

Oct 18 - Oct 12, 2025

• 

  Bio-Inspired Consensus Enabledby Distributed Active Perception

  Fumin Zhang, Chair Professor and Director, Cheng Kar-Shun Robotics Institute, Hong Kong University of Science and Technology

  Oct 13, 12:00 - 13:00

  Auditorium (between Buildings 4 and 5), Level 0, Room 0215

  Consensus is a fundamental requirement for coordination and cooperation among a swarm of intelligent agents. In environments with difficulty for localization and communication, achieving swarm level consensus is a challenging task. We propose a layered approach to achieve signal propagation over large swarms through active perception. Biological evidence from fish swarms has shown that distributed active perception is used by animals to allow fast response to stimulations when only a few members are excited.

Oct 11 - Oct 5, 2025

• 

  FIFA Research Institute

  Silvio Giancola, Research Scientist, Image and Video Understanding Lab

  Oct 6, 12:00 - 13:00

  B9 L2 R2325

  AI Sports FIFA

  This talk will trace the journey from SoccerNet to the creation of the FRI, highlight ongoing projects that bridge cutting-edge research with real-world impact, and discuss what lies ahead at the intersection of AI and sports.

Oct 4 - Sep 28, 2025

• 

  Robotics at KAUST

  Eric Feron, Professor, Electrical and Computer Engineering

  Sep 29, 12:00 - 13:00

  B9 L2 R2325

  robotics

  Robotics at KAUST is a system integration activity that relies on inputs from Computer Science, Software Engineering, Mechanical Engineering, and Electrical Engineering. A few examples of completed and ongoing robotics research at KAUST will be introduced and discussed. In addition, KAUST's new and large robotics bay will be described in detail.

Sep 27 - Sep 21, 2025

• 

  Advice and Comments About Research

  Peter Wonka, Professor, Computer Science

  Sep 22, 12:00 - 13:00

  B9 L2 R2325

  academic life

  This talk provides a collection of advice and commentary on the multifaceted nature of the research process and life as a graduate student.

Sep 20 - Sep 14, 2025

• 

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

  Peter Richtarik, Professor, Computer Science

  Sep 15, 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.

Sep 13 - Sep 7, 2025

• 

  Building a Safer and More Equitable Internet in the Age of AI

  Zafar Qazi, Associate Professor, Computer Science, Lahore University of Management Sciences (LUMS)

  Sep 8, 12:00 - 13:00

  B9 L2 R2325

  This talk will show how interdisciplinary collaboration at the intersection of networked systems, AI, and societal impact can help address challenges related to AI-powered moderation systems guiding access to online resources.