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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.

Department Members in This Field

Faculty

Instructors & Postdocs

  • Sky Cao Probability theory, Yang-Mills
  • Ziang Chen applied analysis, applied probability, statistics, optimization, machine learning
  • Jason Gaitonde Algorithms, Learning Theory, Probability Theory, Networks
  • Yi Han Probability, random matrices
  • Anya Katsevich High dimensional statistics, Bayesian inference
  • Konstantinos Kavvadias Probability and Mathematical Physics
  • Peter Kempthorne Statistics, Financial Mathematics
  • Shivam Nadimpalli Analysis of Boolean Functions, High-Dimensional Geometry, Property Testing
  • Nicholas Nelsen Scientific Machine Learning, Statistics, Inverse Problems
  • Michael Simkin Probabilistic combinatorics, random graphs, and random processes
  • Anirudh Sridhar Statistical inference, network cascades, graph algorithms, graph matching

Researchers & Visitors

  • Ravi Boppana Combinatorics, Discrete Probability, Theoretical Computer Science

Graduate Students*

*Only a partial list of graduate students