Video Lectures

Human memory is a multi-staged phenomenon of extreme complexity, which results in highly unpredictable behavior in real-life situations. Psychologists developed classical paradigms for studying memory in the lab, which produce easily quantifiable...

Given a family of m matchings in a graph G (where each matching can be thought of as a color class), a rainbow matching is a choice of edges of distinct colors that forms a matching. How many matchings of size n are needed to guarantee the existence...

The Large Synoptic Survey Telescope will generate a data deluge: millions of transients and variable sources will need to be classified from their light curves. Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) brings a...

The tasks of finding and randomly sampling solutions of constraint satisfaction problems over discrete variable sets arise naturally in a wide variety of areas, among them artificial intelligence, bioinformatics and combinatorics, and further have...

I will describe the problem of finding the homotopy type of the space of quantum Hamiltonians in dimension d under certain constraints. The common assumptions are that the interactions have finite range and that the ground state is separated from...

The "nearest neighbor (NN) classifier" labels a new data instance by taking a majority vote over the k most similar instances seen in the past. With an appropriate setting of k, it is capable of modeling arbitrary decision rules. Traditional...

In this talk, I'll illustrate how to lift a "small" locally testable code via a high dimensional expander (HDX) to a "large" locally testable code. Given a D-left regular bipartite graph G = ([n], [m], E) and a "small" code C \in {0,1}^D, the Tanner...

In this talk I will discuss some definitions of exponential mixing and other rates of mixing and discuss some of its consequences.

We will discuss the problem of constructing and characterizing uniquely, integral models of Shimura varieties over some primes where non-smooth reduction is expected.

Minimax optimization, especially in its general nonconvex formulation, has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs) and adversarial training. It brings a series of unique...