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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.


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

Tomas Berggren Integrable Probability

Promit Ghosal Probability, Mathematical Physics, Statistics

Jimmy He Probability, Algebraic Combinatorics

Han Huang High dimensional Probability, Random Matrices, Random Graphs

Pakawut Jiradilok Algebraic Combinatorics, Asymptotic Combinatorics, Combinatorial Inequalities, Probability, Statistics

Anya Katsevich Stochastic analysis, interacting particle systems, statistics

Peter Kempthorne Statistics, Financial Mathematics

Dan Mikulincer Probability, High-Dimensional Geometry, Functional Inequalities

Yair Shenfeld Probability, Convex Geometry

Youngtak Sohn Probability, Statistics, Machine Learning

Ilias Zadik Mathematics Of Machine Learning, Information Theory, Statistics, Probability Theory

Graduate Students*

Shrey Aryan Optimal Transport, PDEs and Harmonic Analysis

Sinho Chewi optimal transport, optimization, sampling, statistics

Max Daniels High-dimensional statistics, optimization, sampling algorithms, machine learning

Patrik Gerber

Sergei Korotkikh algebraic combinatorics, integrable probability

Nitya Mani

Ron Nissim Mathematical Physics, Integrable Systems, Stochastic Processes

George Stepaniants Statistical Learning of PDEs, Continuous Neural Networks

Roger Van Peski Integrable probability, algebraic combinatorics, random matrix theory

Pu Yu

*Only a partial list of graduate students