Previous Conferences & Workshops

In this talk, we will introduce a new technique of isolating the cuspidal part, or more general cuspidal components, from the $L^2$ spectrum on the spectral side of the trace formula. As an application, we prove the global Gan-Gross-Prasad and...

Protein-based drugs are becoming some of the most important drugs of the XXI century. The typical mechanism of action of these drugs is a strong protein-protein interaction (PPI) between surfaces with complementary geometry and chemistry. Over the...

I will introduce a math problem from deep learning regarding the regularization effect of gradient flow dynamics for underdetermined problems.

People learn in fast and flexible ways that elude the best artificial neural networks. Once a person learns how to “dax,” they can effortlessly understand how to “dax twice” or “dax vigorously” thanks to their compositional skills. In this talk, we...

This (mostly expository) talk will be about invariant theory viewed through the lens of computational complexity. Invariant theory is the study of symmetries, captured by group actions, by polynomials that are “invariant.” Invariant theory has had a...

I will illustrate a recent result obtained in collaboration with L. Spolaor and B. Velichkov concerning the regularity of the free boundaries in the two phase Bernoulli problems. The new main point is the analysis of the free boundary close to...

We will discuss a recent result with Tristan Buckmaster (Princeton) and Steve Shkoller (UC Davis) which establishes the finite-time shock formation for the 3d isentropic Euler equations with vorticity. We prove that for an open set of Sobolev-class...

We present some work in progress, on moduli spaces of Drinfeld shtukas. These spaces are the function field analogous to Shimura varieties. In fact they are more versatile; there are r-legged versions for any r. Tate's conjecture predicts some...

We recently proposed the "Lottery Ticket Hypothesis," which conjectures that the dense neural networks we typically train have much smaller subnetworks capable of training in isolation to the same accuracy starting from the original initialization...