Carroll symmetries were introduced many years ago by Levy-Leblond and Gupta as a possible contraction of the Lorentz symmetries in which effectively the speed of light is sent to zero. The name was inspired by the bizarre property that Carroll particles cannot move. After many years of silence Carroll symmetries have returned to the stage since they have been recognized as symmetries that do occur in several special situations such as the horizon of a black hole. In this presentation I will discuss some of the basic properties and mysteries of Carroll symmetries.

A few mathematicians had considerable influence during the covid-19 epidemic. Some mathematicians have focussed on designing and implementing mathematical models which only consider a single illness. Applied statisticians know that it is critical to first decide what the question is: "Minimise deaths from Covid-19?" or "Minimise deaths due to Covid-19 and our decisions this year?" or "Minimise the impact of Covid-19 on well-being over ten years?" The ethical status of an expert who gives a simple answer to the first question, without uncertainty or alternatives, will be examined.

Mathematical predictions were used to justify lockdowns even in countries where people would starve as a consequence. Some publications by influential mathematics groups were directly misleading or disparaging of African scientists. Some statisticians have tried to estimate the damage to children's education and wellbeing, and illness and deaths due lockdown.

I argue that such mathematical modelling cannot be justified within virtue, deontological, utilitarian or care ethics, though Zoroaster or Nietzsche might be invoked. It is always necessary to consider the wider context, and the probable consequences of actions, as explained in the International Statistics Institute Code of Professional Ethics. Assessment of the validity of model assumptions, data quality, adequacy of the fit of models and accuracy of predictions is essential, and essentially statistical.

One is sometimes faced with the task of assigning finite values to divergent integrals in a consistent and meaningful way. Since differential forms and integrals play a central role in geometry, how to think about divergent integrals in geometric terms? The goal of this talk will be to answer this question in the case of logarithmic divergences (such as the integral of 1/x near x=0).
The idea is to pass from manifolds to slightly more general objects called manifolds with log corners. They are the differential geometer���s version of logarithmic varieties in algebraic geometry. A key ingredient of our construction is a new notion of morphism between manifolds with log corners (or logarithmic varieties) which is more flexible than the obvious one and faithfully records the geometric information needed to regularize divergent integrals.
This is joint work with Erik Panzer and Brent Pym.

The basic operations of free probability - additive free convolution, multiplicative free convolution, and free compression - describe how the eigenvalues of large random matrices interact under the basic matrix operations, such as addition, multiplication, and taking minors.

In this talk we discuss how these free probability operations can be formulated in terms of an ���entropic optimal transport��� problem ��� an optimal transport problem but with an entropy penalty for the coupling measure.

Our proof of this formulation uses the quadrature formulas of Marcus, Spielman and Srivastava, which relate the expected characteristic polynomial of matrices under random unitary vs symmetric conjugation. The approach involves an asymptotic analysis of the quadrature formulas using a large deviation principle on the symmetric group.

This is joint work with Octavio Arizmendi (CIMAT).

In this talk I will present a solution of the so-called Gaussian level set percolation problem on random graphs. It addresses the question whether a multivariate Gaussian defined on the vertices of a random graph (with inverse covariance matrix defined, e.g., in terms of a weighted graph Laplacian) exhibits a macroscopic contiguous cluster of sites on which the Gaussian exceeds a given level h, or whether on the contrary all such clusters are finite. Because of the correlations encoded in the multivariate Gaussian, the problem is considerably more complicated than the case of independent Bernoulli percolation, and a full solution has, to the best of my knowledge, not been available in the literature. It turns out that the solution of the problem requires control over the microscopic heterogeneity of the percolation problem, i.e. , over distributions of node dependent percolation probabilities on random graphs.

In these lectures we will discuss various aspects of conformal field theories in Lorentzian signature. First, we will study the general properties of Lorentzian correlation functions, including their global conformal structure and the relation to Euclidean correlators. We will then consider the Regge limit of correlation functions and how this limit requires the introduction of complex spin. We will define complex spin using the Lorentzian inversion formula, and interpret it in terms of non-local light-ray operators. Finally, we will discuss applications of light-ray operators to even shape observables.

I will discuss models for longitudinal data, where the data consists of noisy measurements taken at several different time points for each individual, and the aim is to model how each individual's underlying response varies over time. If we assume linear variation of the responses over time, we could use a linear mixed model for this task. In this talk, I will discuss more flexible modelling approaches, which allow the variation of the response over time to be any smooth curve. There is a strong link with models for functional data, and I will describe previous work on adapting methods designed for functional data (where measurements are typically taken very frequently) to longitudinal data (with typically only a few measurements on each individual). However, these existing methods sometimes give fitted mean functions which are more complex than needed to provide a good fit to the data. I will describe a new penalised likelihood approach to flexibly model longitudinal data, with a penalty term to control the balance between fit to the data and smoothness of the subject-specific mean curves. I will show that the new method substantially improves the quality of inference relative to existing methods across a range of simulated examples, and apply the method to data on changes in body composition in adolescent girls.