Past Events

The ability to learn and remember is an essential property of the brain that is not limited to high-level processing. In fact, the perception of olfactory stimuli in rodents is strongly shaped by learning processes in the olfactory bulb, the very...

In this talk, I will exposit some of the main ideas and results of the seminal 2017 paper “The pebbling comonad in finite model theory” by Samson Abramsky, Anuj Dawar, and Pengming Wang. In this paper, the authors demonstrate that pebble games, which...

In 1909, Thue proved that when $F(x,y) \in \mathbb{Z}[x,y]$ is irreducible, homogeneous, and has degree at least 3, the inequality $|F(x,y)| \leq h$ has finitely many integer-pair solutions for any positive $h$. Because of this result, the inequality...

First, I will present a series of classical results on symmetric generating functions and permutation statistics, including the classical results by Carlitz, Roselle and Scoville (1966), by Garsia and Gessel (1978), by Gessel and Reutenauer (1993)...

The question of how roots of polynomials move under differentiation is classical. Contributions to this subject have been made by Gauss, Lucas, Marcel Riesz, Polya and many others. In 2018, Stefan Steinerberger derived formally a PDE that should...

A G2-structure is a special type of 3-form on an oriented 7-manifold, which determines a Riemannian metric in a nonlinear way. The best class of such 3-forms are those which are parallel with respect to their induced Levi-Civita connections, which is...

Recent advances in single cell sequencing has led to the availability of gene expression data. We model the trajectories of cells in gene expression space using a diffusion and gradient drift process that also undergoes branching. In this talk, we...

The moduli space \M_g of genus g curves (or Riemann surfaces) is a central object of study in algebraic geometry. Its cohomology is important in many fields. For example, the cohomology of M_g is the same as the cohomology of the mapping class group...

Although my talk is arguably about machine-learning, I will use mostly ideas and language from mathematical modelling and numerical analysis. I will introduce a natural geometric learning framework, the atomic cluster expansion (ACE), which focuses...

The problem of counting the number of eigenvalues of a differential operator is exactly as old as quantum theory and was inspired by various physical questions. In the century since Weyl’s first asymptotic result on the topic, the question has...