Winter 2021 STAT 38520

Course: STAT 38520=CAAM 38520

Title: Topics in Random Matrix Theory

Instructor(s): Pierre Yves Gaudreau Lamarre

Teaching Assistant(s): TBA

Class Schedule: Sec 1: TR 2:40 PM-4:00 PM (Remote)

Description: Random matrix theory (RMT) is among the most prominent subjects in modern probability theory, with applications in a wide range of disciplines (including physics, statistics, engineering, and finance). The purpose of this course is to study a broad sample of the most prominent research programs in RMT as well as their motivating applications. Main topics will include (time permitting) the moment method in RMT and its connection to combinatorics, universality, operator limits, and matrix concentration.

Prerequisite(s): PhD student in Statistics or Math or Computational and Applied Mathematics or TTIC or MS student in Statistics or Computational and Applied Mathematics. Other students may enroll with consent of instructor.

Note(s): Graduate or advanced undergraduate probability theory and undergraduate linear algebra and combinatorics are recommended.