Past

In this talk, I will discuss a model mixture of active (self-propelled) and passive (diffusive) particles with non-reciprocal effective interactions (or forces that violate Newton’s third law). We derive the hydrodynamic PDE limit for the particle densities, which is not a Wasserstein gradient flow of any free energy, consistent with the microscopic model having non-equilibrium steady states. We study the emergence of collective behaviour, which includes phase separation and dynamical (travelling) steady states.

Guentner, Willet and Yu defined a notion of dynamic asymptotic dimension for an étale groupoid that can be used to bound the nuclear dimension of its groupoid C*-algebra.  To have finite dynamic asymptotic dimension, the isotropy subgroups of the groupoid must be locally finite.  I will discuss 1) how to use similar ideas to bound the nuclear dimension of the C*-algebra of a groupoid with `large' isotropy subgroups and 2) the limitations of that approach. In an application to the C*-algebra of a directed graph,  if the C*-algebra is stably finite, then its nuclear dimension is at most 1.  This is joint work with Dana Williams. 

I will review recent progress on extending the Minimal Model Program to algebraically integrable foliations, focusing on applications such as the canonical bundle formula and recent results toward the boundedness of Fano foliations.

Fundamental to the study of hyperbolic groups is their Gromov boundaries. The classical Cannon--Thurston map for a closed fibered hyperbolic 3-manifolds relates two such boundaries: it gives a continuous surjection from the boundary of the surface group (a circle) to the boundary of the 3-manifold group (a 2-sphere). Mj (Mitra) generalized this to all hyperbolic groups with hyperbolic normal subgroups. A generalization of the Gromov boundary to all finitely generated groups is called the Morse boundary. It collects all the "hyperbolic-like" rays in a group. In this talk we will discuss Cannon--Thurston maps for Morse boundaries. This is joint work with Ruth Charney, Antoine Goldsborough, Alessandro Sisto and Stefanie Zbinden.

Graphs (and structures) which have a linear ordering of their vertices with given local properties have a rich spectrum of complexities. Some have full power of class NP (and thus no dichotomy) but for biconnected patterns we get dichotomy. This also displays the importance of Sparse Incomparability Lemma. This is a joint work with Gabor Kun (Budapest).

The mod-$p$ cohomology of uniform pro-$p$ groups has been calculated by Lazard in the 1960s. Motivated by recent considerations in the mod-$p$ Langlands program, we consider the problem of extending his results to the case of compact $p$-adic Lie groups $G$ that are $p$-saturable but not necessarily uniform pro-$p$: when $F$ is a finite extension of $\mathbb{Q}_p$ and $p$ is sufficiently large, this class of groups includes the so-called pro-$p$ Iwahori subgroups of $SL_n(F)$. In general, there is a spectral sequence due to Serre and Lazard that relates the mod-$p$ cohomology of $G$ to the cohomology of its associated graded mod-$p$ Lie algebra $\mathfrak{g}$. We will discuss certain sufficient conditions on $p$ and $G$ that ensure that this spectral sequence collapses. When these conditions hold, it follows that the mod-$p$ cohomology of $G$ is isomorphic to the cohomology of the Lie algebra $\mathfrak{g}$.

Systems of high-contrast resonators can be used to control and manipulate wave-matter interactions at scales that are much smaller than the operating wavelengths. The aim of this talk is to review recent studies of ordered and disordered systems of subwavelength resonators and to explain some of their topologically protected localization properties. Both reciprocal and non-reciprocal systems will be considered.
 

Given a triangulated 3-manifold, can we decide whether it is hyperbolic? In general, no efficient algorithm for answering this question is known; however, the problem becomes more manageable if we restrict our attention to specific classes of 3-manifolds. In this talk, I will discuss how to certify that a triangulated fibred 3-manifold is hyperbolic, in polynomial time in the size of the triangulation and in the Euler characteristic of the fibre. The argument relies on the theory of normal surfaces, as well as several previously known certification algorithms, of which I will give a survey. I will also mention, time permitting, a recent algorithm to decide if an element of the mapping class group of a surface is pseudo-Anosov in polynomial time, which is used in the certification procedure.

Complex dynamics is the study of the behaviour, under iteration, of complex polynomials and rational functions. This talk is about an application of combinatorial algebraic geometry to complex dynamics. The n-th Gleason polynomial G_n is a polynomial in one variable with Z-coefficients, whose roots correspond to degree-2 polynomials with an n-periodic critical point. Per_n is a (nodal) Riemann surface parametrizing degree-2 rational functions with an n-periodic critical point. Two long-standing open questions are: (1) Is G_n is irreducible over Q? (2) Is Per_n connected? I will sketch an argument showing that if G_n is irreducible over Q, then Per_n is connected. In order to do this, we find a special degeneration of degree-2 rational maps that tells us that Per_n has smooth point with Q-coordinates "at infinity”.

Recent years have witnessed a surge of interest in deep learning methods to tackle inverse problems arising in various domains such as medical imaging, remote sensing, and the arts and humanities. This talk offers an overview of recent advances in the foundations and applications of deep learning for inverse problems, with a focus on model-based deep learning methods. Concretely, this talk will overview our work relating to theoretical advances in the area of mode-based learning, including learning guarantees; algorithmic advances in model-based learning; and, finally it will showcase a portfolio of emerging signal & image processing challenges that benefit from model based learning, including image separation / deconvolution challenges arising in the arts and humanities.

Bio:

Miguel Rodrigues is a Professor of Information Theory and Processing at University College London; he leads the Information, Inference and Machine Learning Lab at UCL, and he has also been the founder and director of the master programme in Integrated Machine Learning Systems at UCL. He has also been the UCL Turing University Lead and a Turing Fellow with the Alan Turing Institute — the UK National Institute of Data Science and Artificial Intelligence.

He held various appointments with various institutions worldwide including Cambridge University, Princeton University, Duke University, and the University of Porto, Portugal. He obtained the undergraduate degree in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto, Portugal and the PhD degree in Electronic and Electrical Engineering from University College London.

Dr. Rodrigues's research lies in the general areas of information theory, information processing, and machine learning. His most relevant contributions have ranged from the information-theoretic analysis and design of communications systems, information-theoretic security, information-theoretic analysis and design of sensing systems, and the information-theoretic foundations of machine learning.

He serves or has served as Editor of IEEE BITS, Editor of the IEEE Transactions on Information Theory, and Lead Guest Editor of various Special Issues of the IEEE Journal on Selected Topics in Signal Processing, Information and Inference, and Foundations and Trends in Signal Processing.

Dr. Rodrigues has been the recipient of various prizes and awards including the Prize for Merit from the University of Porto, the Prize Engenheiro Cristian Spratley, the Prize Engenheiro Antonio de Almeida, fellowships from the Portuguese Foundation for Science and Technology, and fellowships from the Foundation Calouste Gulbenkian. Dr. Rodrigues research on information-theoretic security has also attracted the IEEE Communications and Information Theory Societies Joint Paper Award 2011.  

He has also been elevated to Fellow of the Institute of Electronics and Electrical Engineers (IEEE) for his contributions to the ‘multi-modal data processing and reliable and secure communications.’

We’ll have an open discussion about the ways in which Mathematics is very euro-centric and how we can act, as students and educators, to change this.

For any symplectic singularity, one can consider the minimal degenerations between symplectic leaves - these are the relative singularities of a pair of adjacent leaves in the closure relation. I will describe a complete classification of these minimal degenerations for Nakajima quiver varieties. It provides an effective algorithm for computing the associated Hesse diagrams. In the physics literature, it is known that this Hasse diagram can be computed using quiver subtraction. Our results appear to recover this process. I will explain applications of our results to the question of normality of leaf closures in quiver varieties. The talk is based on joint work in progress with Travis Schedler.

''Positive geometries'' are a class of semi-algebraic domains which admit a unique ''canonical form'': a logarithmic form whose residues match the boundary structure of the domain. The study of such geometries is motivated by recent progress in particle physics, where the corresponding canonical forms are interpreted as the integrands of scattering amplitudes. We recast these concepts in the language of mixed Hodge theory, and identify ''genus zero pairs'' of complex algebraic varieties as a natural and general framework for the study of positive geometries and their canonical forms. In this framework, we prove some basic properties of canonical forms which have previously been proved or conjectured in the literature. We give many examples and study in detail the case of arrangements of hyperplanes and convex polytopes.

[arXiv:2501.03202]

Differentiable programming is the underpinning technology for the AI revolution. It allows neural networks to be programmed in very high level user code while still achieving very high performance for both the evaluation of the network and, crucially, its derivatives. The Firedrake project applies exactly the same concepts to the simulation of physical phenomena modelled with partial differential equations (PDEs). By exploiting the high level mathematical abstraction offered by the finite element method, users are able to write mathematical operators for the problem they wish to solve in Python. The high performance parallel implementations of these operators are then automatically generated, and composed with the PETSc solver framework to solve the resulting PDE. However, because the symbolic differential operators are available as code, it is possible to reason symbolically about them before the numerical evaluation. In particular, the operators can be differentiated with respect to their inputs, and the resulting derivative operators composed in forward or reverse order. This creates a differentiable programming paradigm congruent with (and compatible with) machine learning frameworks such as Pytorch and JAX. 

In this presentation, David Ham will present Firedrake in the context of differentiable programming, and show how this enables productivity, capability and performance to be combined in a unique way. I will also touch on the mechanism that enables Firedrake to be coupled with Pytorch and JAX.

Please note this talk will take place at Rutherford Appleton Laboratory, Harwell Campus, Didcot. 

In the first half of the talk I will review the theory of nuclear magnetic resonance (NMR), leading to the Bloch-Torrey PDE. I will then describe the pulsed-gradient spin-echo method for measuring the Fourier transform of the voxel-averaged propagator of the Bloch-Torrey equation.  This technique permits one to compute the diffusion coefficient in a voxel. For complex biological tissue, as in the brain, the standard model represents spin-echo as a multiexponential signal, whose exponents and coefficients describe the diffusion coefficients and volume fractions of isolated tissue compartments, respectively. The question of identifying these parameters from experimental measurements leads us to investigate the degree of well-posedness of this problem that I will discuss in the second half of the talk. We show that the parameter reconstruction problem exhibits power law transition to ill-posedness, and derive the explicit formula for the exponent by reformulating the problem in terms of the integral equation that can be solved explicitly. This is a joint work with my Ph.D. student Henry J. Brown.

In this talk, we consider Dirichlet forms related to stochastic quantisation for the \(exp(φ)_{2}\)-model on the torus. We show strong uniqueness of the corresponding Dirichlet operators by applying an idea of (singular) SPDEs. This talk is based on ongoing joint work with Hirotatsu Nagoji (Kyoto University).

This will be an expository lecture intended for a general mathematical audience to illustrate, through examples, the theme of $p$-adic variation in the classical theory of modular forms.  Classically, modular forms are complex analytic objects, but because their Fourier coefficients are typically integral, it is possible to also do elementary arithmetic with them.   Early examples arose already in the work of Ramanujan.  Today one knows that modular forms encode deep arithmetic information about elliptic curves and Galois representations.  Our main goal will be to illustrate these ideas through simple concrete examples.   

Multiparameter persistent homology is a generalization of persistent homology that allows for more than a single filtration function. Such constructions arise naturally when considering data with outliers or variations in density, time-varying data, or functional data. Even though its algebraic roots are substantially more complicated, several new invariants have been proposed recently. In this talk, I will go over such invariants, as well as their stability, vectorizations and implementations in statistical machine learning.

In this Fridays@4 event – for this week renamed Fridays@1 (with lunchtime pizza) – Torkel Loman from the Mathematics Institute and Alastair McCullough from the Department of Computer Science will present their talks.

Torkel Loman
The behaviours of noisy feedback loops and where (in parameter space) to find them

Alastair McCullough 
Tech, Coffee, and the Regulation of Truth: An Enterprise Barista's Story

Torkel's abstract

Mixed positive/negative feedback loops (networks where a single component both activates and deactivates its own productions) are common across biological systems, and also the subject of this talk. Here (inspired by systems for e.g. bacterial antibiotics resistance), we create a minimal mathematical model of such a feedback loop. Our model (a stochastic delay differential equation) depends on only 6, biologically interpretable, parameters. We describe 10 distinct behaviours that such feedback loops can produce, and map their occurrence across 6-dimensional parameter space.

A monoid S is said to be weakly right coherent if every finitely generated right ideal of S is finitely presented as a right S-act. It is known that S is weakly right coherent if and only if it satisfies the following conditions: S is right ideal Howson, meaning that the intersection of any two finitely generated right ideals of S is finitely generated; and the right annihilator congruences r(a)={(u,v) in S x S | au=av} for each a in S are finitely generated as right congruences.

This talk will introduce basic semigroup theoretic concepts as is necessary before briefly surveying some important coherency-related results. Closure properties of the classes of monoids satisfying each of the above properties will be shared, with details explored for a specific construction. Time permitting, connections with axiomatisation will be discussed.

This talk will in part be based on a paper written with coauthors Craig Miller and Victoria Gould, preprint available at: arXiv:2411.03947.

Consumer-resource interactions are central to ecology, as all organisms rely on consuming resources to survive. Functional responses describe how a consumer's feeding rate changes with resource availability, influenced by processes like searching for, capturing, and handling resources. To study functional responses, experiments typically measure the amount of food consumed—often in discrete units like prey—over a set time. These experiments systematically vary prey availability to observe how it affects the consumer's feeding behaviour. The data generated by such experiments are often analysed using differential equation-based models. Here, we argue that such models do not represent a realistic data-generating process for many such experiments and propose an alternative stochastic individual-based model. This class of models, however, is expensive for inference, and we use machine learning methods to expedite fitting these models to data. We then use our method to do generalised linear model-based inference for a series of experiments conducted on a stickleback fish. Our methodology is made available to others in a Python package for Bayesian hierarchical inference for stochastic, individual-based models of functional responses.