About me
I am a 4th-year Ph.D. student in Computer Science at the University of Chicago, working at the Chicago Human + AI Lab (CHAI) and advised by Professor Chenhao Tan. My research focuses on natural language processing (NLP), explainable AI, and hypothesis generation, with the goal of creating interpretable AI systems that support scientific discovery and deepen our understanding of the physical world.
Outside of research, I enjoy playing the piano, tennis, and experimenting in the kitchen.
Publications
Haokun Liu, Sicong Huang, Jingyu Hu, Yangqiaoyu Zhou, and Chenhao Tan, HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation, 2025, preprint.
Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, Chenfei Yuan, and Chenhao Tan, Literature Meets Data: A Synergistic Approach to Hypothesis Generation, 2025, ACL.
Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava, Hongyuan Mei, and Chenhao Tan, Hypothesis Generation with Large Language Models, 2024, EMNLP workshop on NLP for Science.
Elena Orlova, Haokun Liu, Raphael Rossellini, Benjamin A Cash, and Rebecca Willett, Beyond ensemble averages: Leveraging climate model ensembles for subseasonal forecasting, 2024, Artificial Intelligence for the Earth Systems, 3(4), e230103.
Updates
April 24, 2025: Checkout the first principled benchmark on hypothesis generation! HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation - Available at: https://chicagohai.github.io/HypoBench/
October 15, 2024: We updated our Hypothesis generation framework to support using both literature and data! - Available at: https://github.com/ChicagoHAI/hypothesis-generation