I am a fourth-year PhD student at CMU CSD, co-advised by Zico Kolter and Pradeep Ravikumar. I study machine learning theory and algorithms. My primary interest are representation learning and generalization theory, including statistical generalization and out-of-distribution (OOD) generalization. I am developing self-supervised methods for learning representations that are more controllable and interpretable than the prevailing black-box AI models, and applying these methods to various domains. I also work on general statistical learning theory.

I received my Bachelor's degree in computer science and applied math (double degree) from Peking University, where I was advised by Liwei Wang. I 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.

I am enthusiastic about mentoring students, especially students who just started doing research. Several of my past mentees have published their first ML papers at top venues, and been hired by top tech companies such as OpenAI and Amazon. For junior students: Please feel free to contact me by email if you are interested in working with me, especially if you want to work on representation learning, self-supervised learning, or generalization theory.

Peer review:

• JMLR
• ICLR 2023 - 2024
• NeurIPS 2022 - 2023
• ICML 2022 - 2024
• AISTATS 2023 - 2024
• KDD 2023 - 2024
• ICCV 2023
• SDM 2024
• ICLR workshop: BGPT'24
• NeurIPS workshops: M3L'23, R0-FoMo'23, ML-Safety'22, TSRML'22
• ICML workshop: PODS'22

Teaching:

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