privacy-preserving 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.

KAUST-CEMSE-CS-PhD-Dissertation-Defense-Juexiao-Zhou

Towards Privacy-preserving Artificial Intelligence (PAI) for Healthcare and Bioinformatics

Juexiao Zhou, Ph.D. Student, Computer Science

May 20, 09:00 - 11:00

B3, L5, Room 5209

privacy-preserving AI Federated learning healthcare applications

This thesis presents innovative privacy-preserving AI solutions for healthcare and bioinformatics, including federated learning for multi-omics, data deletion from deep learning models, and on-device medical analysis with vision LLMs, advancing secure and ethical AI development.

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).