I am an Assistant Professor in the Department of Statistics and the Department of Electrical Engineering and Computer Sciences at UC Berkeley. In June 2020, I received my Ph. D. from Stanford, where I was advised by Andrea Montanari.

My research is motivated by data science, and lies at the intersection of statistics, machine learning, information theory, and computer science. My current research interests include high dimensional probability, theory of deep learning , and theory of reinforcement learning.

Here is my Google Scholar page and my Research Blog.
Email: songmei@berkeley.edu
Office: Evans 387.

Recent works

Multi-agent reinforcement learning

Our goal is to develop efficient algorithms for learning equilibria in multi-player games. Applications include real-world games such as Poker, Bridge, and Go, as well as economic systems such as auction and marketing.

Deep learning and invariances

Our goal is to theoretically investigate the interplay between convolution structures in neural networks and the image distribution. We explained the mechanism of why convolutional neural networks (kernels) are suitable for image datasets.

High dimensional statistics

We study statistical-computational gaps in high dimensional statistical models.