Our summer school is motivated by the observation that the breadth of random matrix theory and the maturity of some of its branches can be overwhelming for graduate students and junior researchers in the subject. Though a student or researcher may become familiar with one aspect of random matrix theory, it can be challenging to learn complementary techniques requiring an entirely different background. The goal of the summer school is to provide an opportunity for graduate students (and postdocs) to learn techniques in random matrix theory outside their specialties and acquire the background necessary to understand how and when to apply these techniques to new problems.
Apply HERE before February 1, 2026.
(Princeton University, Department of Mathematics and PACM)
Lecture title: Intrinsic freeness
(Princeton University, Department of Mathematics)
Lecture title: Strong convergence and its applications
9:00–10:30
Lecture by Ramon van Handel (Title: Intrinsic freeness)
12:30–1:30
Lunch (on your own)
1:30–3:00
Lecture by Jorge Garza-Vargas (Title: Strong convergence and its applications)
(McGill University, Department of Mathematics and Statistics)
Lecture title: Optimization and random matrix theory
(University of Pennsylvania, Department of Statistics and Data Science)
Lecture title: Edge statistics of sparse random graphs
9:00–10:30
Lecture by Courtney Paquette (Title: Optimization and random matrix theory)
12:30–1:30
Lunch (on your own)
1:30–3:00
Lecture by Jiaoyang Huang (Title: Edge statistics of sparse random graphs)
A key component of the summer school is participants' working together in (assigned) groups to solve problems related to the morning and afternoon lectures.
Emma Bailey giving a lecture in 2022
Some 2022 participants went to Detroit during the weekend.