Previous Conferences & Workshops

Dec
16
2020

Mathematical Conversations

The perceptron problem
Nike Sun
5:30pm|Remote Access

In high dimensions, what does it look like when we take the intersection of a set of random half-spaces with either the sphere or the Hamming cube? This is one phrasing of the so-called perceptron problem, whose study originated with a toy model of...

Dec
16
2020

Geometric and Modular Representation Theory Seminar

Hecke category via derived convolution formalism
Dima Arinkin
3:00pm|Remote Access

The talk is about convolution in the setting of geometric representation theory. What are its formal properties? As a starting point, let $G$ be a group and let $D(G)$ be the derived category of constructible sheaves on it. Convolution turns $D(G)$...

Dec
16
2020

Stability and Testability

Hilbert-Schmidt stability of groups via C*-algebras
Tatiana Shulman
11:00am|Remote Access

The aim of this talk is to show that C*-algebras are useful for studying stability of groups. In particular we will discuss some obstructions for Hilbert-Schmidt stability of groups, obtain a complete characterization of Hilbert-Schmidt stability...

Dec
15
2020

SL2 Seminar

Cohomology: qualitative and stable results
Eric M. Friedlander
3:00pm|Remote Access

We survey work over the last 50 years advancing our understandingof cohomology of groups. We begin with results of Daniel Quillen which have influenced all that follows. We mention stability results of Cline, Parshall, Scott, and van der Kallen...

Dec
14
2020

Analysis Seminar

The singular set in the Stefan problem
Joaquim Serra
4:30pm|Remote Access

The Stefan problem, dating back to the XIX century, aims to describe the evolution of a solid-liquid interface, typically a block of ice melting in water. A celebrated work of Luis Caffarelli from the 1970's established that the ice-water interface...

Dec
14
2020

Members’ Seminar

A Feynman Approach to Dynamic Rate Markov Processes
William A. Massey
2:00pm|Simonyi Hall 101 and Remote Access

Physics inspired mathematics helps us understand the random evolution of Markov processes. For example, the Kolmogorov forward and backward differential equations that govern the dynamics of Markov transition probabilities are analogous to the...