My research interest lies at the span of high-dimensional probability and statistics, causal inference, and machine learning. In particular, I mainly work on the following topics:
Theory of statistics: high-dimensional and non-parametric statistics.
Causal inference with an emphasis on (non-asymptotic) theoretical viewpoints.
Machine learning theory: statistical foundations of reinforcement learning, optimization, and learning in games.
Probability theory: learning in the space of probability measures (e.g., sampling algorithms, diffusion models, and geometry of the space of probability measures).