About Me

I recently received my PhD in Computer Science at Stanford University, advised by Emma Brunskill. I was also a member of the Stanford AI Lab and the Statistical Machine Learning Group there. During my PhD, I had the fortune of collaborating with Finale Doshi-Velez from Harvard, Adith Swaminathan and Alekh Agarwal from Microsoft Research. I also spent times at Simons Institute for the Theory of Computing, Microsoft Research (NYC lab and Redmond), and Livongo Health (a digital health start-up helping chronic condition patients). Before 2017, I was a Ph.D. student at CMU advised by Emma Brunskill. Before that, I obtained my B.S. from the Department of Machine Intelligence at Peking University in 2016, and learned about machine learning from Liwei Wang.

A key aspect of AI is to make decisions, beyond to perceive and to predict. Thus it need to interact with environments. I am interested in interactive machine learning (e.g. reinforcement learning, imitation learning, contextual bandit) under the real-world constraints about sample efficiency and safety. I have been working on the following aspects of this problem:

I am also interested in problems about causality and fairness in interactive learning.

Preprints and Publications

Teaching

CS234: Reinforcement Learning, Teaching Assistant, Winter 2019-2020.

CS229: Machine Learning, Teaching Assistant, Spring 2020-2021.

Professional Service

Journal Reviewing: Biometrika, JMLR, IEEE TPAMI, Machine Learning, Artificial Intelligence

Conference Reviewing: NeurIPS (2019 - ), ICLR (2019 - ), ICML(2020 - ), AISTATS (2020 - ), UAI(2020)