I am an Assistant Professor of Statistics and Data Science at Yale University.
Past Visits
I received my B.S. in Engineering Mathematics and Statistics from the University of California, Berkeley in 2018, and my PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023, advised by Philippe Rigollet. In Fall 2021, I participated in the Simons Institute program on Geometric Methods in Optimization and Sampling and co-organized (with Kevin Tian) a working group on the complexity of sampling. In Spring 2022, I visited Jonathan Niles-Weed at New York University. In Summer 2022, I was a research intern at Microsoft Research, supervised by Sébastien Bubeck and Adil Salim. In Fall 2023 and Spring 2024, I was a postdoctoral researcher at the Institute for Advanced Study.
I am currently writing a book on the complexity of log-concave sampling. You can read the current draft here.
Any feedback is appreciated!
Last Updated: April 18, 2024
Jonathan Niles-Weed, Philippe Rigollet, and I wrote a monograph on statistical optimal transport, which grew out of lectures given at the École d’Été de Probabilités de Saint-Flour (Saint-Flour Probability Summer School) by Philippe when he was invited as a lecturer there in 2019. You can also find it on arXiv.
I am broadly interested in the mathematics of machine learning and statistics. My work focuses on applications of optimal transport to computational problems arising in these fields, such as log-concave sampling (see my book draft above) and variational inference (Lambert et al. (2023); Diao et al. (2023); Jiang, Chewi, and Pooladian (2024)).
PhD thesis: An optimization perspective on log-concave sampling and beyond. 2023.
Click here to find the notes I took and courses I taught during my undergraduate and graduate studies.