PACM Alumni Panel

I am an assistant professor of Statistics and Data Science at Yale University. Previously, I was a Stein Fellow in the Department of Statistics at Stanford University (2022–2025). I obtained my Ph.D. in Applied and Computational Mathematics from Princeton University in May 2022, under the supervision of Professor Allan Sly and Professor Emmanuel Abbe. Prior to Princeton, I received my B.Sc. in Mathematics from the University of Hong Kong. My research interests include probability theory, high-dimensional statistics, theoretical machine learning, and the theory of algorithms.

I am an Assistant Professor of Applied Mathematics and of Computer Science at Harvard, where I lead the Geometric Machine Learning Group. My research focuses on utilizing geometric structure in data for the design of efficient Machine Learning and Optimization methods with provable guarantees. This AI Magazine article surveys Geometric Machine Learning, including my work within this area.

In 2021-2022, I was a Hooke Research Fellow at the Mathematical Institute in Oxford and a Nicolas Kurti Junior Research Fellow at Brasenose College. In Fall 2021, I was a Research Fellow at the Simons Institute in Berkeley, where I participated in the program Geometric Methods for Optimization and Sampling. Previously, I received my PhD from Princeton University (2021) under the supervision of Charles Fefferman, held visiting positions at MIT and the Max Planck Institute for Mathematics in the Sciences and interned in the research labs of Facebook, Google and Microsoft.

My research is supported by the National Science Foundation, DARPA, the Alfred P. Sloan Foundation, the Aramont Foundation, the Harvard Dean’s Fund and the Harvard Data Science Initiative.

I am a Research Scientist at the Center for Computational Mathematics, Flatiron Institute. Previously, I worked as an Instructor of Mathematics at the Department of Mathematics, Princeton University. I obtained my Ph.D. degree in applied mathematics from the Program in Applied and Computational Mathematics (PACM), Princeton University in June 2018, advised by Prof. Weinan E. Before that, I received my Bachelor degree from the School of Mathematical Sciences, Peking University in July 2013. I did a research internship in DeepMind during the summer of 2017, under the mentorship of Thore Graepel.

I conduct research on machine learning for science, drawing broadly from the methodologies and challenges of various scientific disciplines, with a focus on solving high-dimensional problems in scientific computing, primarily those related to PDEs and generative modeling. My research has been recognized with the SIAM Computational Science and Engineering (CSE) Early Career Prize (awarded biennially to one scholar).