Berkeley Statistical Machine Learning
We are a group of researchers at UC Berkeley working in statistical machine learning.
Topics of recent interest include:
- Bayesian computation
- Data-driven decision making
- Foundations of deep learning
- Interpretable machine learning
- Language and diffusion models
- Nonparametric inference
- Probabilistic forecasting
- Randomized algorithms
- Reinforcement learning
- Statistical learning theory
- Stochastic optimization
- Uncertainty quantification
- Variational inference
- Veridical data science
News
Current semester (Spring 2026) group meetings listed below. Everyone is welcome! But please send an email first if you’re a new participant.
- Nika Haghtalab: Thursdays at 10am (contact Nika to find location).
- Jiantao Jiao: Mondays at 4:30pm in Cory 258.
- Michael Jordan: Mondays at 11:30am in Soda 510.
- Michael Mahoney: Thursdays at 2pm in Soda 465H.
- Ryan Tibshirani: Tuesdays at 4pm in Evans 1011.
- Bin Yu: Wednesday at 9:30am in Evans 1011.
- Simons ML reading group: Mondays at 2pm in Calvin Lab 116.
Peter Bartlett will give a plenary lecture at International Congress of Mathematicians (ICM) in Philadelphia in July 2026.
Tom Mitchell gave a special talk on the History of Machine Learning on March 3, 2026 at 4pm in Evans 1011. [Podcast]
Bin Yu gave the opening plenary talk at the International Conference on Statistics and Data Science (ICSDS) in December 2025 in Seville, Spain. [Slides]
Faculty
Postdocs
Students
