Han Zhao | 赵晗


Assistant Professor
Department of Computer Science
Department of Electrical and Computer Engineering (affiliated)
University of Illinois at Urbana-Champaign
Email: hanzhao [AT] illinois (DOT) edu
Office: 3320 Siebel Center, 201 N Goodwin Ave Urbana, IL, 61801
[Curriculum Vitae] [Google Scholar] [DBLP] [Thesis]

About Me

I am an assistant professor at the Department of Computer Science, University of Illinois at Urbana-Champaign, also affiliated with the Department of Electrical and Computer Engineering. Before joining UIUC, I was a machine learning researcher at D. E. Shaw & Co. I obtained my Ph.D. from the Machine Learning Department, Carnegie Mellon University, where I was advised by the great Geoff Gordon. During my Ph.D., I have also been closely working with Ruslan Salakhutdinov. Previously, I obtained my BEng degree from the Computer Science Department at Tsinghua University and MMath from the University of Waterloo.

I have a broad interest in both the theoretical and applied side of machine learning. In particular, I work on transfer learning (domain adaptation/generalization, multitask/meta-learning), algorithmic fairness, probabilistic circuits, and their applications in natural language, signal processing and quantitative finance. My long-term goal is to build trustworthy ML systems that are efficient, robust, fair, and interpretable.

Prospective students, please read this.

Publications [ show selected / show by date ]

Pre-prints

Learning List-Level Domain-Invariant Representations for Ranking
R. Xian, H. Zhuang, Z. Qin, H. Zamani, J. Lu, J. Ma, K. Hui, H. Zhao, X. Wang, M. Bendersky
arXiv preprint
[abs] [pdf]
Understanding the Impact of Adversarial Robustness on Accuracy Disparity
Y. Hu, F. Wu, H. Zhang, and H. Zhao
arXiv preprint
[abs] [pdf]
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
W. Chu, C. Xie, B. Wang, L. Li, L. Yin, H. Zhao, B. Li
arXiv preprint
[abs] [pdf]
Costs and Benefits of Fair Regression
H. Zhao
arXiv preprint
[abs] [pdf]
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations
Y. H. Tsai, M. Q. Ma, H. Zhao, K. Zhang, L-P. Morency, R. Salakhutdinov
arXiv preprint
[abs] [pdf]

People

Yuzheng Hu (PhD student)
Seiyun Shin (PhD student, co-advised with Ilan Shomorony)
Haoxiang Wang (PhD student, co-advised with Bo Li)
Ruicheng Xian (PhD student)
Gargi Balasubramaniam (MSCS)
Yifei He (MSCS)
Haozhe Si (MSEE)
Aditya Sinha (MSCS)
Qilong Wu (MSCS)
Siqi (Cindy) Zeng (undergrad @ CMU Math)

Teaching

Term Course Location Time
Fall 2021 CS 598 - Special Topics: Transfer Learning Siebel Center 0216 WF 2PM - 3:15PM
Spring 2022 CS 442 - Trustworthy Machine Learning Siebel Center 1109 WF 3:30PM - 4:45PM
Fall 2022 CS 498 ML - Trustworthy Machine Learning 4025 Campus Instructional Facility TR 2PM - 3:15PM
Spring 2023 CS 598: Transfer Learning Siebel Center 0216 WF 12:30PM - 1:45PM

Misc

I enjoy sketching and calligraphy at my spare time. If I have a long vacation, I also enjoy traveling.