Han Zhao

Han Zhao is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Department of Electrical and Computer Engineering. He is also an Amazon Scholar at Amazon AI and Search Science. Prior to UIUC, he was a machine learning researcher at D.E. Shaw & Co. Zhao holds a Ph.D. from Carnegie Mellon University's Machine Learning Department, an MMath from the University of Waterloo, and a BEng from Tsinghua University's Computer Science Department.

His research focuses on trustworthy machine learning, emphasizing transfer learning (domain adaptation, generalization, multitask/meta-learning), algorithmic fairness, and probabilistic circuits. Applications span natural language processing, signal processing, and quantitative finance. He aims to develop robust, fair, and interpretable ML systems.

Recent work includes advancements in domain adaptation theory, multi-task learning optimization, and fair classification post-processing. He advises numerous PhD and master’s students across CS and ECE, co-advising some with colleagues like Hari Sundaram and Ilan Shomorony. Courses taught include CS 442 (Trustworthy ML) and CS 446 (Machine Learning).

Key contributions include the MDAN framework for multi-source domain adaptation and theoretical analyses of invariant representation learning. His work balances foundational theory with practical applications, addressing challenges like hyperparameter sensitivity and scalable influence functions.