Previous Conferences & Workshops

Understanding deep learning calls for addressing three fundamental questions: expressiveness, optimization and generalization. Expressiveness refers to the ability of compactly sized deep neural networks to represent functions capable of solving...

Chernoff-type bounds study concentration of sums of independent random variables and are extremely useful in various settings. In many settings, the random variables may not be completely independent but only have limited independence. One such...

The study of eigenvarieties began with Coleman and Mazur, who constructed the first eigencurve, a rigid analytic space parametrizing $p$-adic modular Hecke eigenforms. Since then various authors have constructed eigenvarieties for automorphic forms...

The uncertainty principle says that a function and its Fourier transform can not be well-localized simultaneously. We will first discuss a version of this statement for a collection of functions forming a basis for $L^2$ space. Then we will connect...

Ratner's celebrated equidistribution theorem states that the trajectory of any point in a homogeneous space under a unipotent flow is getting equidistributed with respect to some algebraic measure. In the case where the action is horospherical, one...

This talk will give a detailed introduction to the proof of Zimmer's conjecture made in recent work with Brown and Hurtado. The talk will be independent of Monday's member seminar, but I will not repeat history and motivation. I will begin by (re...

I will present a computation of the integral p-adic etale cohomology of the Drinfeld half-space. Via integral p-adic Hodge Theory of Bhatt-Morrow-Scholze this reduces to a computation of the integral de Rham cohomology and this can be done...

We will discuss several examples where ideas from persistent homology have applications to the study of the coarse geometry of groups of Hamiltonian diffeomorphisms, equipped with the Hofer norm..

The key idea is to consider Hamiltonian Floer...