Probability & Statistics
Following the work of Kolmogorov and Wiener, probability theory after WW II concentrated on its connections with PDEs and harmonic analysis with great success. It deserves credit for some of the most delicate results in modern harmonic analysis; it provides the foundation on which signal processing and filtering theory are built in engineering; and it played a critical role in the mathematical attempts to rationalize quantum field theory. Combinatorial branches of probability theory were overshadowed during that period but are now returning to the fore. Probability theory lies at the crossroads of many fields within pure and applied mathematics, as well as areas outside the boundaries of the mathematics department. Statistics is a mathematical field with many important scientific and engineering applications.
Faculty
Alexei Borodin Integrable Probability
Elchanan Mossel Probability, Algorithms and Inference
Philippe Rigollet Statistics, Machine Learning
Scott Sheffield Probability and Mathematical Physics
Nike Sun Probability, statistical physics
Instructors & Postdocs
Souvik Dhara Percolation, Random graphs, Phase transition, Large deviation, Graph limits
Promit Ghosal Probability, Mathematical Physics, Statistics
Jimmy He Probability, Algebraic Combinatorics
Peter Kempthorne Statistics, Financial Mathematics
Dan Mikulincer Probability, High-Dimensional Geometry, Functional Inequalities
Yair Shenfeld Probability, Convex Geometry
Yilin Wang Probability theory, complex analysis and mathematical physics
Ilias Zadik Mathematics Of Machine Learning, Information Theory, Statistics, Probability Theory
Graduate Students*
Sinho Chewi optimal transport, optimization, sampling, statistics
Sergei Korotkikh algebraic combinatorics, integrable probability
George Stepaniants Statistical Learning of PDEs, Continuous Neural Networks
Roger Van Peski Integrable probability, algebraic combinatorics, random matrix theory
*Only a partial list of graduate students