In the talk, we will discuss the irreducibility and the Galois group of random polynomials over the integers. After giving motivation (coming from work of Breuillard--Varju, Eberhard, Ferber--Jain--Sah--Sawhney, and others), I will present a result, conditional on the extended Riemann hypothesis, showing that the characteristic polynomial of certain random tridiagonal matrices is irreducible, with probability tending to 1 as the size of the matrices tends to infinity. 

The proof involves random walks in direct products of \({\rm SL}_2(\mathbb{F}_p)\), where we use results of Breuillard--Gamburd and Golsefidy--Srinivas. 

Joint work with Lior Bary-Soroker and Sasha Sodin.

Some Shimura varieties are moduli spaces of Abelian varieties with extra structure.

The Tate module of a universal Abelian variety is a natural source of $\ell$-adic local systems on such Shimura varieties. Remarkably, the theory allows one to build these local systems intrinsically from the Shimura variety in an essentially tautological way, and this construction can be carried out in exactly the same way for Shimura varieties whose moduli interpretation remains conjectural.

This suggests the following program: Show that these tautological local systems "look as if" they were arising from the cohomology of geometric objects. In this talk, I will describe some recent progress. It is based on joint work with Kiran Kedlaya, Christian Klevdal, and Stefan Patrikis, as well as joint work with Yifei Zhang.

[pre-talk at 3pm]

Kinetoplast DNA, often described as molecular chainmail, is found in the mitochondria of trypanosome parasites and consists of thousands of topologically interlocked circular molecules. In addition to its biological role in gene editing, it has been explored recently as a model system for materials science, due to its unique topological connectivity and its two-dimensional structure. In this talk, I will discuss some mathematical investigations that have emerged out of materials-based research of kinetoplast DNA, including the relationship between the link topology of the network and the Gaussian curvature of chainmail membranes, as well as methods to detect Borromean linking within densely linked networks.

In recent years, tensor networks have emerged as a powerful low-rank approximation framework for addressing exponentially large data science problems without requiring exponential computational resources. In this talk, we demonstrate how tensor networks, when combined with accelerations from randomized numerical linear algebra (rNLA), can enable the efficient representation and manipulation of large-scale, complex datasets originating from quantum physics, high-dimensional function approximation, and neural network compression. We will start by describing how to construct a tensor network directly from input data. Building on this foundation, we then describe a new randomized algorithm called Successive Randomized Compression (SRC) that asymptotically accelerates the tensor network analog of matrix-vector multiplication using the randomized singular value decomposition. As a demonstration, we present examples showing how tensor network based simulations of quantum dynamics in 2^100 dimensions can be performed on a personal laptop.