Philippe Rigollet

Philippe Rigollet is the Cecil and Ida Green Distinguished Professor of Mathematics at the Massachusetts Institute of Technology, where he was appointed full professor in July 2020. He is also affiliated with MIT's Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). His academic journey spans positions at Princeton University (2008-2014) and Georgia Tech (2007-2008) before joining MIT in 2015.

Rigollet's educational background includes a PhD in mathematical statistics from the University of Paris VI (now Sorbonne University) in 2006, an M.Sc. in Statistics & Actuarial Science from ISUP in 2003, and a B.Sc. in Applied Mathematics from the University of Paris VI in 2002, along with a B.Sc. in Statistics in 2001.

His research spans statistics, machine learning, and optimization, with particular focus on high-dimensional problems, statistical limitations of learning under computational constraints, statistical optimal transport, and the mathematical foundations of transformer models. Rigollet approaches interdisciplinary problems at the intersection of computer science and statistics, bringing insights from each field to the other. His work has evolved from theoretical investigations of statistical versus computational trade-offs to practical applications in areas like cryoelectron microscopy and biological data analysis.

Analysis of his recent publications reveals a strong trend toward developing mathematical frameworks for understanding modern AI architectures, particularly transformer models, through the lens of optimal transport and dynamical systems. His work connects deep learning theory with classical statistical methods, creating bridges between seemingly disparate mathematical domains.

Notable scientific achievements:

• NSF CAREER Award (2011/2015) for Large Scale Stochastic Optimization and Statistics
• Elected Fellow of the Institute of Mathematical Statistics (2021) for contributions to statistical versus computational trade-offs, theory of aggregation, and statistical optimal transport
• Frank E. Perkins Award for Excellence in Graduate Advising (2023)
• Best Paper Award at Conference On Learning Theory (COLT) (2013)
• Invited Speaker at International Congress of Mathematicians (2026)

Rigollet has advised numerous PhD students, many of whom have secured prestigious academic positions at institutions including Yale, NYU, Georgia Tech, and Duke. His research group at MIT includes current PhD students and postdoctoral researchers working on topics ranging from mathematical foundations of transformers to biological applications of optimal transport. He maintains active collaborations with the Broad Institute, particularly through the Eric and Wendy Schmidt Center, applying novel mathematical methods to genomic data analysis.