Published multiple papers at top conferences such as NeurIPS 2025, ICML 2025, AISTATS 2025, covering topics from thought communication to nonparametric factor analysis.
Research Experience
Lead coordinator for Causal-learn, an open-source Python package for causal discovery, offering implementations of up-to-date causal discovery methods and simple, intuitive APIs.
Education
PhD student at Carnegie Mellon University (CMU), advised by Prof. Kun Zhang; Bachelor's degree from UESTC in 2021, with time spent at UC Berkeley and Taiwan Tech.
Background
Research interests include causal representation learning, trustworthy machine learning, and foundation models. Part of the CMU-CLeaR group.