Georgia Institute of Technology College of Sciences

We will explore some of the basic notions in quantum topology.  Our focus will be on introducing some of the foundations of diagrammatic algebra through the lens of the Temperley-Lieb algebra.  We will attempt to show how these diagrammatic techniques can be applied to low dimensional topology.  Every effort will be made to make this as self-contained as possible.  If time permits we will also discuss some applications to topological quantum computing.

Understanding the folding of RNA sequences into three-dimensional structures is one of the fundamental challenges in molecular biology.  For example, the branching of an RNA secondary structure is an important molecular characteristic yet difficult to predict correctly.  However, recent results in geometric combinatorics (both theoretical and computational) yield new insights into the distribution of optimal branching configurations, and suggest new directions for improving prediction accuracy.

A multivariate complex polynomial is called stable if any line in any positive direction meets its hypersurface only at real points.  Stable polynomials have close relations to matroids and hyperbolic programming.  We will discuss a generalization of stability to algebraic varieties of codimension larger than one.  They are varieties which are hyperbolic with respect to the nonnegative Grassmannian, following the notion of hyperbolicity studied by Shamovich, Vinnikov, Kummer, and Vinzant. We show that their tropicalization and Chow polytopes have nice combinatorial structures related to braid arrangements and positroids, generalizing some results of Choe, Oxley, Sokal, Wagner, and Brändén on Newton polytopes and tropicalizations of stable polynomials. This is based on joint work with Felipe Rincón and Cynthia Vinzant.

In this talk we introduce some machine learning problems in the setting of undirected graphical models, also known as spin systems. We take proper colorings as a representative example of a hard-constraint graphical model. The classic problem of sampling a proper coloring uniformly at random of a given graph has been well-studied. Here we consider two inverse problems: Given random colorings of an unknown graph G, can we recover the underlying graph G exactly? If we are also given a candidate graph H, can we tell if G=H? The former problem is known as structure learning in the machine learning field and the latter is called identity testing. We show the complexity of these problems in different range of parameters and compare these results with the corresponding decision and sampling problems. Finally, we give some results of the analogous problems for the Ising model, a typical soft-constraint model. Based on joint work with Ivona Bezakova, Antonio Blanca, Daniel Stefankovic and Eric Vigoda.