How would we get a powerful AI to align itself with human preferences? What are human preferences anyway? And how can you code all this?
It turns out that maths give you the grounding to answer these fascinating and vital questions.
Let's take a step back to understand what it means to use maths in society: Which maths, and whose society? I'll talk about some of the options I've come across, including time I spent at the US Census Bureau, and we will hear your ideas too. We might even crowdsource a document of maths in society opportunities together...
The $k$-th complete homogeneous symmetric polynomial in $m$ variables $h_{k,m}$ is the sum of all the monomials of degree $k$ in $m$ variables. They are related to the Symmetric powers of vector spaces. In this talk we will present some of their standard properties, some classic combinatorial results using the "stars and bars" argument, as well as an interesting result: the complete homogeneous symmetric polynomial applied to $(1+X_i)$ can be written as a linear combination of complete homogeneous symmetric poynomials in the $X_i$. To compute the coefficients of this linear combination, we extend the classic "stars and bars" argument.
Molecules are dynamical systems that can adopt a variety of three dimensional conformations which, in general, differ in energy and physical properties. The identification of energetically favourable conformations is fundamental in molecular physics and computational chemistry, since it is closely related to important open problems such as the prediction of the folding of proteins and virtual screening for drug design.
In this talk I will present theoretical and data-driven approaches to the study of molecular conformational spaces and their associated energy landscapes. I will show that the topology of the internal molecular conformational space might change after taking its quotient by the group action of a discrete group of symmetries. I will also show that geometric and topological tools for data analysis such as procrustes analysis, local dimensionality reduction, persistent homology and discrete Morse theory provide with efficient methods to study the mathematical structures underlying the molecular conformational spaces and their energy landscapes.
The reach is an important geometric invariant of submanifolds of Euclidean space. It is a real-valued global invariant incorporating information about the second fundamental form of the embedding and the location of the first critical point of the distance from the submanifold. In the subject of geometric inference, the reach plays a crucial role. I will give a new method of estimating the reach of a submanifold, developed jointly with Clément Berenfeld, Marc Hoffmann and Krishnan Shankar.
The talk will introduce two general models of random simplicial complexes which extend the highly studied Erdös-Rényi model for random graphs. These models include the well known probabilistic models of random simplicial complexes from Costa-Farber, Kahle, and Linial-Meshulam as special cases. These models turn out to have a satisfying Alexander duality relation between them prompting the hope that information can be transferred for free between them. This turns out to not quite be the case with vanishing probability parameters, but when all parameters are uniformly bounded the duality relation works a treat. Time permitting I may talk about the Rado simplicial complex, the unique (with probability one) infinite random simplicial complex.
This talk is based on various bits of joint work with Michael Farber, Tahl Nowik, and Lewin Strauss.
Finitely presented groups are a natural algebraic generalisation of the collection of finite groups. Unlike the finite case there is almost no hope of any kind of classification.
The goal of random groups is therefore to understand the properties of the "typical" finitely presented group. I will present a couple of models for random groups and survey some of the main theorems and open questions in the area, demonstrating surprising correlations between these probabilistic models, geometry and analysis.