I am a fourth-year PhD student at CMU CSD, co-advised by Zico Kolter and Pradeep Ravikumar. I am broadly interested in statistical learning theory, and developing more white-box learning algorithms and architectures that come with provable guarantees. Currently I am investigating the mathematical structure of representation learning, including how it generalizes with big models and finite samples, and how it represents real-world signals with low-dimensional features. Related topics include kernel methods, dimensionality reduction and semi-supervised learning. I am also working on OOD generalization, that is how a model can generalize to a test data distribution different from the training distribution.

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.

Peer review:

• ICLR 2023, 2024
• AISTATS 2023, 2024
• NeurIPS 2022, 2023
• ICML 2022, 2023
• ICCV 2023
• SDM 2024
• KDD 2023
• JMLR
• 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)