AI

The Three Pillars of Practical Machine Learning

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This thesis establishes methods, evaluation protocols, and insights toward machine learning systems that are practical, adaptive, efficient, and trustworthy paving path for practical machine learning that rests on key three pillars: adaptation, efficiency, and trustworthiness.

Causal Reasoning in Medical Digital Twins: Methods and Architectures

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B3 R5209

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.

Protecting the systems that power modern life

4 min read ·
The modern world runs on interconnected cyber-physical and distributed digital systems. When cyber-physical systems are compromised, the consequences can extend into the physical world. A cyberattack on a maritime system can disrupt supply chains. A breach in financial infrastructure can destabilize economies. An autonomous system exploit can paralyze transportation networks. These scenarios raise a fundamental question: how can digital and cyber-physical systems be designed to remain secure and resilient in increasingly complex environments? For three decades, KAUST Professor Roberto Di

Private and Robust Learning for Decision Making

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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.

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

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B9 R2325

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.

Synergizing Nanophotonics and Artificial Intelligence: From Inverse Design to Intelligent Optical Frontends

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B3 R5220

This dissertation investigates the transformative convergence of nanophotonics and artificial intelligence (AI).

What Survives When Code Doesn’t?

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B9 R2325

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.

Can AI Make Physicians Better at Diagnosis?

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B9 R2325

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.

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

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B9 R2325

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.

Provable and Measurable Machine Unlearning in Modern Learning Systems

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B2 L5 R5209

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.

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

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B9 L2 R2325

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

From Prompts to Production: The Systems Agenda for Agentic AI

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B9 L2 R2325

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.

KAUST, in collaboration with MCIT, to host the second edition of the MENA Machine Learning Winter School (MenaML)

1 min read ·
A week of advanced instruction and exchange for emerging machine learning talent.

Data-driven Anomaly Detection in Industrial Processes

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B9 L2 R2325

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.

From Dialects to Peptides: Scalable and Efficient AI for People

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B9 L2 R2325

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.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations
Visiting student at King Abdullah University of Science and Technology (KAUST).

Uncertainty-Aware Learning: From Bayesian Neural Networks to Agentic Decision Making

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B4/5 L0 A0215

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.

Thoughts About Machine Learning

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B9, Lecture Hall 1, R-2322

A weekly seminar series from January 26 to April 20, 2026, exploring advanced AI concepts beyond the scope of standard deep learning courses.

KAUST showcases its smart health technologies at global forum

1 min read ·
Through AI, smart sensors, and computational biology, KAUST is transforming healthcare with practical solutions that address critical health needs in Saudi Arabia and beyond.

Rising Stars in AI Symposium 2026

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KAUST Campus

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

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Overcoming Catastrophic Forgetting: From Efficiency to Safety

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B3 L5 R5220

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.

Mathematical Imaging in the Era of AI

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B1 L23 R3119

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.

Nuclear Fusion Powered by AI and HPC

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B9 L2 R2325

This talk presents advanced simulations with AI-driven optimization to improve the performance of a next-generation plasma-jet-driven magneto-inertial fusion concept.

KAUST researcher proves the power of homegrown talent on the world stage

1 min read ·
A look into KAUST Ph.D. candidate Mohammed Aljahdali’s journey and the FLTA Best Paper award recognizing his work in federated learning.

Artificial intelligence takes to the skies to protect a Saudi tradition

1 min read ·
KAUST scientists develop an affordable and simple AI system for local camel herders

AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory

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B9 L3 R3120

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.

Deloitte and KAUST join hands to explore AI applications in Saudi Arabia

2 min read ·
Partnership will focus on research, education, and real-world AI solutions to help advance Vision 2030

LLM Injection Cyber Resilient Assistants

LLM injection-resilient cyber assistants using Constitutional AI guardrails, Adaptive Constitutional AI guardrails, DPO, and Unlearning.

FIFA Research Institute

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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.

AI for Science: AI-Driven Knowledge Discovery

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B1 L3 R3119

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.

AI speeds up human embryo model research

1 min read ·
Deep-learning tool outperforms human experts at classifying lab-grown embryo-like structures, enabling advances in developmental biology and drug discovery.

Advanced Dynamic and Static Malware Analysis using LLMs

The increasing complexity of malware highlights the need for advanced analysis tools, both static and dynamic, for effective reverse engineering and behavioral analysis of a given sample. While static methods such as disassembly and code review remain crucial, many malware samples use packers and obfuscation techniques that necessitate memory captures and dynamic analysis [Dynamic, 2012]. Similarly, hooking system and API calls at lower levels provides a more comprehensive view of a program’s true behavior. It enables analysts to capture transient execution stages in a multi-layered malware

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

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B1 L3 R3119

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.

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

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B9 L2 R2325

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.

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

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B9 L2 R2325

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.

Bridging AI and Spatial Biology to Navigate Biological Landscapes

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This thesis presents several studies by proposing AI-driven methods to supply a comprehensive and precise representation of biological landscapes via spatially resolved transcriptomics (SRT) data.

SAAI for Kids hackathon introduces Saudi youth to creative uses of AI

3 min read ·
More than 200 students joined KAUST’s SAAI Hackathon to explore AI in art, music and design

AI for Chips and Chips for AI

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B9 L2 R2325

This seminar presents a unified "AI for Chips & Chips for AI" approach, demonstrating how AI enhances semiconductor design while specialized silicon accelerates AI computation, creating a rapid innovation cycle.

Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

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B2/3 L0 R0215

This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.

SAAI Factory 3

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Buildings 20 and Buildings 19

SAAI Factory 3 brings together artists, researchers and youth to explore AI and art at KAUST.
Rising Stars in AI" Symposium 2025

Rising Stars in AI Symposium 2025

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Building 19, Halls 1, 2 and 3

AI

Join KAUST's Rising Stars in AI Symposium 2025, bringing together exceptional emerging talent shaping the future of AI research and innovation.

KAUST developing robotic system to improve date palm harvesting

1 min read ·
Combination of AI and robotics positions Saudi Arabia to transform date farming.
kaust-iit-collaboration-life-sciences-nanotechnology-research

KAUST collaboration to benefit life sciences and nanotechnology research

1 min read ·
KAUST and IIT unite to enhance research in life sciences, AI, robotics and nanotech, driving innovation through collaboration.
optoelectronic device can adapt to light

Artificial neurons enable neuromorphic computing with light

1 min read ·
An adaptive capacitor that can sense and learn through optical stimuli enables learning and recognition tasks.
AI tool maps hidden links between diseases

AI tool maps hidden links between diseases

1 min read ·
Understanding how diseases trigger one another could lead to better treatments, prevention strategies and improved genetic risk prediction.
Olga Krestinskaya

International recognition for KAUST Ph.D. candidate Olga Krestinskaya

3 min read ·
Olga Krestinskaya, a KAUST Ph.D. student, wins Best Poster at the 2024 Nature Neuromorphic Computing Conference for her work on energy-efficient AI hardware.
Sustainable solutions in water conservation

KAUST-Engineered tool drives sustainable solutions in water conservation

3 min read ·
KAUST’s startup EcoStream cuts water waste by 50% while promoting affordability and sustainability, winning the 2024 StartSmart prize.

Aznaur Aliev

M.S. Student

Mhd. Emad Alolaby

Mohammed A Shukri

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

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B3 L5 R5209

Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems

Omar Khalifa

PhD student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST) ,working under the supervision of Prof. Tareq Al-Naffouri.

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science at King Abdullah University of Science and Technology (KAUST) working in Professor Bernard Ghanem research group Image and Video Understanding Lab (IVUL).

Hasan Abed Al Kader Hammoud

Hamzeh Abu Qamar

MS Student, Electrical, Electronics and Communications Engineering at King Abdullah University of Science and Technology (KAUST).

Albaraa Omar Alrehaili

Visiting Student (former), Electrical and Computer Engineering
Intern student in the Electrical Engineering Department, King Abdullah University of Science and Technology (KAUST).