Trustworthy AI

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.

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.

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.

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.

Mehdi Zakroum

Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Web Security Trustworthy AI

Yulian Wu

Ph.D. Student, Computer Science

machine learning privacy Trustworthy AI decision making decision sciences privacy-preserving AI trustworthy machine learning Robustness Reinforcement Learning quantum machine learning bioinformatics direct preference optimization

Yulian Wu is a Ph.D. candidate under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST).

Cheng-Long Wang

Ph.D. Student, Computer Science

Machine Unlearning Data Privacy Trustworthy AI Federated learning

Cheng-Long Wang is a PhD candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), advised by Prof. Di Wang.

Ali Shoker

Research Associate Professor, Computer Science

cybersecurity resilient computing resilience blockchain resilient cyber-physical distributed systems Satellite Security autonomous driving intrusion tolerance Trustworthy AI malware SOC Radio Resilience

Ali Shoker, a Research Associate Professor at KAUST leading the Cyber Security and Resilience Technology lab. His research is focused on Applied Cyber Security and Resilience for real-world applications including SOC (SIEM, SOAR, XDR, IDS, IPS), Trustworthy AI, satellite systems, autonomous vehicles, fintech, Blockchain, RF Resilience, etc.