Past Events

At a time when many are wrangling with biological ‘big data’, there remain important problems that are fundamentally data limited – often physiological questions for which there is little quantitative data, and further data collection may be hampered...

I will explain how the Geometric Lemma allows us to classify parabolically induced representations of the p-adic group $G_2$ distinguished by $SO_4$. In particular, I will describe a new approach, in progress, where we use the structure of the p-adic...

I will review some of the basic machinery used in formulating characteristic classes for coherent sheaves on complex manifolds. The main ideas go back to the fundamental work of Toledo and Tong in the 70s. A natural extension of their ideas leads to...

In this talk, I will describe how certain non-linear exponential tilts of the Discrete Gaussian Free Field yield invariant measures for a discretized PDE, the focusing Non Linear Schr\”odinger equation. I will briefly talk about how this tilted...

Intraguild predation (IGP) consists of two (or more) consumers of the same shared resource exhibiting a predator-prey relation among themselves, and is a very present phenomena in terrestrial, freshwater and marine ecological systems. Theoretical...

The notions of Hausdorff and Fourier dimensions are ubiquitous in harmonic analysis and geometric measure theory. It is known that any hypersurface in ℝd+1 has Hausdorff dimension d. However, the Fourier dimension depends on the finer geometric...

I will discuss the fourth moment of quadratic Dirichlet L-functions where we prove an asymptotic formula with four main terms unconditionally. Previously, the asymptotic formula was established with the leading main term under generalized Riemann...

POSTPONED: Unfortunately this event has been postponed at short notice. It will no longer take place on March 18th, but will be rescheduled for later in the summer About Land and Sea: Mathematics of the Climate Crisis-A Global Conversation is the...

Since shortly after the popularization of stochastic gradient optimization methods in machine learning---which now scale model training to billions of examples and beyond---researchers have been trying to use the same basic data subsampling...

Single-cell omics data play a pivotal role in identifying cell-to-cell heterogeneity, understanding cell differentiation, unveiling cell population structures, and ultimately deciphering disease pathogenesis. Due to inherent high-dimensionality...