Trustworthy AI

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

Yibo Yang

Research Scientist

machine learning large languaje models Trustworthy AI

Yibo Yang is a research scientist at the Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem. Before joining KAUST, he obtained his Ph.D. from Peking University in 2021. He worked as a researcher at JD Explore Academy, JD.com, in 2021-2023.

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.