Song Mei is an Assistant Professor in statistics at UC Berkeley. His research is motivated by data science and lies at the intersection of statistics, machine learning, information theory, and computer science. His work often builds on insights that originate within statistical physics literature. His recent research interests include theory of deep learning, high dimensional geometry, approximate Bayesian inferences, and applied random matrix theory.
- Computational Complexity of Statistical Inference, Fall 2021. Visiting Scientist.
- Geometric Methods in Optimization and Sampling, Fall 2021. Visiting Scientist.
- Probability, Geometry, and Computation in High Dimensions, Fall 2020. Visiting Scientist.