Han Liu obtained his Ph.D. in computer science from the University of Chicago, where he was a member of the Chicago Human+AI Lab (CHAI Lab), led by Prof. Chenhao Tan. His research focused on human–AI collaboration, delving into critical areas such as AI explainability, alignment, and machine teaching.

Fascinated by the relationship between humans and AI, Han explored how they can best interact and collaborate. He envisioned a future where humans harness the power of AI to address some of the world's most significant challenges.

He earned his Bachelor's degree in mathematics, computer science, and linguistics at Washington University in St. Louis, where he cultivated a deep interest in artificial intelligence and natural language processing. He also spent a year as a graduate student at the University of Colorado Boulder before moving to Chicago.

Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted Prostate Cancer MRI Diagnosis
Chacha Chen, Han Liu, Jiamin Yang, Benjamin M. Mervak, Bora Kalaycioglu, Grace Lee, Emre Cakmakli, Matteo Bonatti, Sridhar Pudu, Osman Kahraman, Gül Gizem Pamuk, Aytekin Oto, Aritrick Chatterjee, and Chenhao Tan.
Accepted at ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025).

Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, and Yuxin Chen.
In the International Conference on Learning Representations (Spotlight, ICLR 2024).

Learning Human-Compatible Representations for Case-Based Decision Support
Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, and Chenhao Tan.
In the International Conference on Learning Representations (ICLR 2023).

Aligning Offline Metrics and Human Judgments of Value of AI-Pair Programmers
Victor Dibia, Adam Fourney, Gagan Bansal, Forough Poursabzi-Sangdeh, Han Liu, Saleema Amershi.
In Findings of the Association for Computational Linguistics: ACL 2023 (Findings of ACL 2023).

Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Han Liu, Vivian Lai, and Chenhao Tan.
In Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2 (CSCW 2021).

"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
Vivian Lai, Han Liu, and Chenhao Tan.
In Proceedings of the 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020).

Morphology Matters: A Multilingual Language Modeling Analysis
Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley, Kenneth Steimel, Han Liu, and Lane Schwartz.
Transactions of the Association for Computational Linguistics, 9 (TACL 2021).

Neural Polysynthetic Language Modelling
Lane Schwartz, Francis Tyers, Lori Levin, Christo Kirov, Patrick Littell, Chi-kiu Lo, Emily Prud'hommeaux, Hyunji Hayley Park, Kenneth Steimel, Rebecca Knowles, Jeffrey Micher, Lonny Strunk, Han Liu, Coleman Haley, Katherine J. Zhang, Robbie Jimmerson, Vasilisa Andriyanets, Aldrian Obaja Muis, Naoki Otani, Jong Hyuk Park, Zhisong Zhang.
Technical Report (2020)

Quantifying the polymerization dynamics of plant cortical microtubules using kymograph analysis
Benjamin Zhou, Han Liu, Tao Ju, and Ram Dixit.
Methods in Cell Biology, 160. (2020)