I am a third-year PhD student of CMU SCS Computer Science Department, co-advised by Zico Kolter and Pradeep Ravikumar. My research interest is the fundamental theory of learning, and furthermore how to close the gap between theory and practice. I am most interested in generalization theory, and recently I am mainly focusing on analyzing the generalization of representation learning, especially with big models, with kernel and spectral graph theory. I am also studying OOD generalization, which is how a model can generalize to a test data distribution different from the training distribution, and related topics include domain adaptaion, continual learning, algorithmic fairness, etc.

I received my Bachelor's degree in computer science and applied math (double degree) from Peking University. As an undergraduate I was advised by Liwei Wang. I also visited UCLA in the summer of 2019 and worked with Cho-Jui Hsieh. In the summer of 2022, I worked at Amazon Alexa AI at Sunnyvale as an applied scientist intern. From Sept 2019 to Jun 2020 I worked as a full-time research intern in Microsoft Research Asia (MSRA) machine learning group at Beijing.

Peer review:

• ICML 2022, 2023
• ICCV 2023
• KDD 2023
• AISTATS 2023
• ICLR 2023
• NeurIPS 2022, 2023
• JMLR
• NeurIPS workshops: ML Safety'22, TSRML'22
• ICML workshop: PODS'22

Teaching:

• CMU 10-701: Introduction to Machine Learning Fall 2022 (Head TA)