I am a Ph. D. student major in applied mathematics and statistics at Stanford, advised by Andrea Montanari. Before coming to California, I studied Mathematics at Peking University. My research is partly supported by Stanford Graduate Fellowship.
My research is motivated by data science, and lies at the intersection of statistics, machine learning, information theory, and computer science. I often build on insights that originated within the statistical physics literature.
Currently, I am interested in theory of deep learning, high dimensional geometry, approximate Bayesian inferences, and applied random matrix theory.
Honors & Awards
Stanford Graduate Fellowship, Stanford University (2014-2017)
- The Landscape of the Spiked Tensor Model COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS 2019; 72 (11): 2282–2330
- THE LANDSCAPE OF EMPIRICAL RISK FOR NONCONVEX LOSSES ANNALS OF STATISTICS 2018; 46 (6): 2747–74
- A mean field view of the landscape of two-layer neural networks PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2018; 115 (33): E7665–E7671
- ON A MOLECULAR BASED Q-TENSOR MODEL FOR LIQUID CRYSTALS WITH DENSITY VARIATIONS MULTISCALE MODELING & SIMULATION 2015; 13 (3): 977-1000