I am a third-year PhD student of CMU SCS Computer Science Department, co-advised by Zico Kolter and Pradeep Ravikumar. My research area is machine learning, and I am deeply interested in bridging the gap between machine learning theory and applications. Recently I am mainly focusing on the theoretical perspective of representation learning, such as how to explain the success of big models pretrained with BERT, contrastive learning and so on with kernel and spectral graph theory, and how to design new pretrain tasks using these theoretical frameworks. I am also studying OOD generalization, where the data distribution on which the model is trained is different from the testing data distribution, and related topics include domain adaptaion, continual learning, algorithmic fairness, semi-supervised learning and self-supervised learning. In addition, I am also working on general optimization and generalization theory.

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
• JMLR
• NeurIPS workshops: ML Safety'22, TSRML'22
• ICML workshop: PODS'22

Teaching:

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