Associate Professor of Mathematics
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. He has held positions as a visiting assistant professor at the Georgia Institute of Technology and then as an assistant professor at Princeton University.
- "Online learning in repeated auctions," Jonathan Weed, Vianney Perchet and Philippe Rigollet (2016), Proceedings of COLT 2016. J. Mach. Learn. Res. W&CP, 49.
- "Batched Bandit Problems," Vianney Perchet, Philippe Rigollet, Sylvain Chassang, and Erik Snowberg (2016), Ann. Statist., 44(2), 660-681.
- "Complexity Theoretic Lower Bounds for Sparse Principal Component Detection," Quentin Berthet and Philippe Rigollet (2013), Proceedings of COLT 2013. J. Mach. Learn. Res. W&CP, 30, 1046-1066. Best Paper Award
- "Optimal detection of sparse principal components in high dimension," Quentin Berthet and Philippe Rigollet (2013), Ann. Statist., 41(1), 1780-1815.
- "The multi-armed bandit problem with covariates," Vianney Perchet and Philippe Rigollet (2013), Ann. Statist., 41(2), 693-721.
- 2016. NEC Corporation Fund for Research in Computers and Communications
- 2015. Invited lecturer. Machine Learning Summer School, Kyoto, Japan
- 2013. Best Paper Award. Conference on Learning Theory (COLT)
- 2013. Howard B. Wentz Jr. Junior Faculty Award
- 2011. NSF CAREER Award DMS-1053987
- 18.650 Statistics for Applications