I received my Ph.D. from Princeton, where I was advised by Sanjeev Arora (research group page, ML theory at Princeton).

I focus on machine learning theory and applied probability, and also have broad interests in theoretical computer science and related math.

Research interests

Although machine learning (and deep learning in particular) has made great advances in recent years, our mathematical understanding of it is shallow. Learning problems can be highly nonconvex, yet tractable in practice. What hidden structure do these problems have, and how can we design algorithms to take advantage of it?

Current interests include:


The publication list is available as pdf.

[A] denotes alphabetical order of authors.

Machine learning

Bayesian inference and sampling algorithms

Reinforcement learning and control theory

Neural networks

Complexity theory

Number theory