About Me

I am a Ph.D. student at Stanford University, advised by Emma Brunskill. Before 2017, I was a Ph.D. student at CMU advised by Emma Brunskill. I obtained my B.S. in Machine Intelligence from Peking University in 2016. As an undergraduate, I was interested in machine learning theory and optimization, and had the fortune of doing research under the supervision of Liwei Wang.


I am motivated by advancing reinforcement learning (RL) in real-world applications where sample cost and safety would be huge challenges. Thus I am interested in RL algorithms that collect and use sample more efficiently with provable guarantees, especially in off-line manner. The main problems I am interested in are also known as efficient exploration and off-policy (batch) RL. I am also attracted by other problems about learning from interactions, including contextual bandits problem, imitation learning, and causal inference. I am interested in applications of these methods in helping more people, for examples healthcare and education.


I will visit Simons Institute for the theory of reinforcement learning program in this fall.




Workshop Papers


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

Professional Service

Journal Reviewing: Journal of Machine Learning Research (JMLR)

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


A collection of my interests out of reasearch area.