In this talk, we discuss nearly G2 structures, which define positive Einstein metrics, and are, up to scale, critical points of a geometric flow called (modified) Laplacian co-flow. We will discuss a recent joint work with Jason Lotay showing that many of these nearly G2 critical points are unstable for the flow. 

In this talk I will describe a possible strategy to obtain new solution to LMCF in the Kummer K3 surface by a fixed point argument. The key idea is that the regions where curvature concentrates in the Kummer K3 surface are modeled on the Eguchi-Hanson space. 

Temperley-Lieb algebras are certain finite-dimensional algebras coming originally from statistical physics and knot theory. Around 2019, they became one of the first examples of homological stability for algebras (homology is here taken to be certain Tor-groups), when Boyd and Hepworth showed that in low dimensions the homology vanishes. We're now able to give complete calculations of their homology, which has a surprisingly rich structure (and in particular is very far from vanishing). This is joint work in progress with Rachael Boyd, Oscar Randal-Williams, and Robin Sroka. Prerequisites will be minimal: it will be enough to know what Tor is.

Since their introduction in 2017, Transformers have revolutionized large language models and the broader field of deep learning. Central to this success is the ground-breaking self-attention mechanism. In this presentation, I’ll introduce a mathematical framework that casts this mechanism as a mean-field interacting particle system, revealing a desirable long-time clustering behaviour. This perspective leads to a trove of fascinating questions with unexpected connections to Kuramoto oscillators, sphere packing, Wasserstein gradient flows, and slow dynamics.

Bio: Philippe Rigollet is a Distinguished Professor of Mathematics at MIT, where he serves as Chair of the Applied Math Committee and Director of the Statistics and Data Science Center. His research spans multiple dimensions of mathematical data science, including statistics, machine learning, and optimization, with recent emphasis on optimal transport and its applications. See https://math.mit.edu/~rigollet/ for more information.

This talk is hosted by the AI Reading Group