Previous Conferences & Workshops

In this talk, we will discuss some of the recent advances in high-dimensional robust statistics. In particular, we will focus on designing faster and simpler robust algorithms for fundamental statistical and machine learning problems.

Online learning is a popular framework for sequential prediction problems. The standard approach to analyzing an algorithm's (learner's) performance in online learning is in terms of its empirical regret defined to be the excess loss suffered by the...

We make the case that over the coming decade, computer assisted reasoning will become far more widely used in the mathematical sciences. This includes interactive and automatic theorem verification, symbolic algebra, and emerging technologies such...

The importance of analyzing big data and in particular very large networks has shown that the traditional notion of a fast algorithm, one that runs in polynomial time, is often insufficient. This is where property testing comes in, whose goal is to...

Given an increasing family F in {0,1}^n, its measure according to mu_p increases and often exhibits a threshold behavior, growing quickly as p increases from near 0 to near 1 around a specific value p_c. Thresholds of families have been of great...

In this talk I will outline recent work in collaboration with Pierre Germain, Zaher Hani and Jalal Shatah regarding a rigorous derivation of the kinetic wave equation. The proof presented will rely of methods from PDE, statistical physics and number...

I will discuss recent results on localization for the Anderson--Bernoulli model. This will include my work with Ding as well as work by Li--Zhang. Both develop new unique continuation results for the Laplacian on the integer lattice.

This is an update on the talk I gave about 10 years ago on this seminar. The arithmetic Gan-Gross-Prasad (AGGP) conjecture, a high dimensional generalization of the Gross–Zagier theorem, relates the height pairing of arithmetic diagonal cycles on...