Philippe Rigollet works at the intersection of statistics, machine learning, and optimization, focusing primarily on the design and analysis of statistical methods for high-dimensional problems. His recent research focuses on the statistical limitations of learning under computational constraints. At the University of Paris VI, Rigollet earned a BS in statistics in 2001, a BS in applied mathematics in 2002, and a PhD in mathematical statistics in 2006 under the supervision of Alexandre Tsybakov. Prior to joining MIT in 2015, he held positions as a visiting assistant professor at the Georgia Institute of Technology, and assistant professor at Princeton University.
- Geometric Methods in Optimization and Sampling, Fall 2021. Workshop Organizer.
- Probability, Geometry, and Computation in High Dimensions, Fall 2020. Workshop Organizer.
- Bridging Continuous and Discrete Optimization, Fall 2017. Workshop Organizer.
- Foundations of Machine Learning, Spring 2017. Visiting Scientist.