Statistics, Probability, Analysis and Applied Mathematics (SPAAM)

SPAAM Seminar Series 2025/26

The Statistics, Probability, Analysis and Applied Mathematics (SPAAM) seminar series will take place on Thursdays between 3-4pm in room B3.02 and virtually on the SPAAM Microsoft Teams ChannelLink opens in a new window during term time. It will host a variety of talks from PhD students involved in applied mathematics research at Warwick and invited guests from other institutions (see below for the schedule and talk abstracts).

The seminars will usually host two speakers (unless otherwise stated) with each talk taking around 15-20 minutes with 5-10 minutes of questions afterwards. Speakers and committee members will hang around after the talks for social tea/coffee (and cakes!) and further questions.

This seminar series is hosted by the Warwick SIAM-IMA Student ChapterLink opens in a new window. Please do contact one of the committee members if you would like to join and be added to the MS Teams channel. Note that these talks may be recorded so do join with audio and video off if you don't wish to feature!

If you missed the seminar, head over to our YouTube channelLink opens in a new window to find the recorded talks!

If you would like to give a talk this academic year, please contact one of our seminar organisers:

Sasha Glendinning ( )

Zeynep Tunalioglu ( )

and we will find you a slot!

Find out more about the
Warwick SIAM-IMA Student Chapter

Term 1

Date	Talk 1	Talk 2
16th October (Week 2)	Eva Zaat	Ruibo Kou
23rd October 2024 (Week 3)	Chess Tournament Social
30th October 2024 (Week 4)	Jiayao Shao	Matthew Adeoye
6th Nov 2024 (Week 5)	Freddie Jensen	Marc Truter
13th November 2024 (Week 6)	Ziyang Liu	Usman Ladan
20th November 2024 (Week 7)	Hefin Lambley	Sam Turley
27th November 2024 (Week 8)	Andrew Nugent	Ramon Nartallo-Kaluarachchi
4th December 2024 (Week 9)	Mario Kart Social
11th December 2024 (Week 10)	Grega Saksida	Shreya Sinha Roy

Term 1 Abstracts

Week 2.

Eva Zaat (Warwick Mathematics Institute) | Making Maths 'Easier'

Maths isn't always easy, but sometimes it is harder than it needs to be. In this talk we will discuss some practical strategies to make maths communication and problem solving more accessible for everyone. We will start with how mathematicians solve their problems, before exploring how maths anxiety, neurodivergence and (other) disabilities can impact how we engage with maths. By understanding these different experiences better, we can support ourselves, our collaborators, our audiences and our students; reduce barriers to learning maths and create more inclusive maths spaces.

Ruibo Kou (Warwick Mathematics Institute) | The Stochastic Casimir Effect

We model the one-dimensional ‘classical’ vacuum by a system of annihilating Brownian motions on R with pairwise immigration. A pair of reflecting or absorbing walls placed in such a vacuum at separation L experiences an attractive force which decays exponentially with L. This phenomenon can be regarded as a purely classical Casimir effect for a system of interacting Brownian motions.

Week 4.

Jiayao Shao (Warwick Mathematics Institute) | Stochastic Dynamical System Methods applied to ship capsize problem

Stochastic dynamical systems allow modelling of transitions induced by disturbances, in particular from an attracting equilibrium and crossing the stable manifold of a saddle. In the small-noise limit, the probability of such transitions is governed by a large deviation principle. We illustrate a computational approach—the Method of Division—for approximating rare transition events, including their most likely paths, transition rates, and associated probabilities. To cater for realistic applications, we allow unbounded time, coloured and degenerate forcing. Its effectiveness is demonstrated via a ship capsize model.

Matthew Adeoye (Warwick Department of Statistics) | Bayesian spatio-temporal modelling for infectious disease outbreak detection

Surveillance of infectious diseases across multiple regions often requires the use of spatio-temporal models to characterize transmission dynamics and detect outbreaks. However, these models can suffer from high computational costs, parameter identifiability issues, and difficulties in comparing alternative model structures. In this talk, I will present a general and scalable Bayesian methodology that addresses these challenges, providing efficient posterior inference and a robust model comparison technique to approximate model evidence in high-dimensional space. To conclude, I will present some results from application to both simulated and real-world datasets.

Week 5.

Freddie Jensen (Warwick Mathematics Institute) | Observations from modelling nonlinear acoustics in brass instruments

We discuss interesting features of a recently-developed model which combines weak nonlinearity and complex geometry in duct acoustics without flow, with applications to sound in brass instruments. Topics discussed here include curvature-induced plane-wave tunnelling, a method of quantifying the speed of sound around bends, an ambiguity around forward/backward decomposition, and a new test case in the study of nodes and turning points.

Marc Truter (Warwick Mathematics Institute) | Using Deep Reinforcement Learning to Build a Periodic Table of Shapes

In this talk we show how deep reinforcement learning can be used to overcome the computational challenges faced by conventional search algorithms in the discovery of new Fano hypersurfaces. Varieties are the core objects of study in algebraic geometry, they are the geometric shapes defined by the solutions to polynomial equations. An ‘atomic’ and important class of these are called Fano varieties. Periodic tables of them are known in dimensions 1 and 2, and partially known in dimension 3. The goal of my project is to help build a periodic table in dimension 4.

Week 6.

Ziyang Liu (Warwick Mathematics Institute) | An Ito’s formula of the stochastic heat flow

The 2d critical stochastic heat flow is the solution to the multiplicative stochastic heat equation in the critical dimension 2. In a recent work of Makoto Nakashima, the stochastic heat flow is considered in a martingale setting, hence leading to a study of an associated Ito’s formula. In this talk, I will briefly introduce the stochastic flow as a scaling limit of a discrete polymer model and discuss its martingale structure and associated Ito’s formula.

Usman Ladan (Warwick Department of Statistics) | An introduction to branching processes and their approximations

In this talk, we introduce branching processes and discuss the behaviour of both discrete and continuous time models. These objects have applications in a wide variety of areas such as biology and physics. We will also discuss how to efficiently approximate these objects via mutation/selection type schemes.

Week 7.

Hefin Lambley (Warwick Mathematics Institute) | Learning in function space: theory and practice

I will discuss neural operators: generalisations of neural networks to inputs and outputs that are functions rather than vectors. I will show that many existing machine-learning methods can be lifted to function space, and explain some challenges in doing so. I will then show some of the highly successful applications of neural operators in scientific machine learning to regression and generative modelling.

Sam Turley (Warwick Mathematics Institute) | Causal Entropic Force - Intelligent Decision Making

I will be introducing Causal Entropic Forces, which introduced intelligent decision-making by driving systems to maximize the diversity of possible future states. We will look at toy examples and model systems before moving onto the application to collective motion. Here, each particle will move and re-orient following this intelligent decision-making framework, and we'll observe some of the complex emergent dynamics that arise.

Week 8.

Andrew Nugent (UCL) | Existence of stationary distributions in an age-structured model of opinion formation
Models of opinion formation describe how a population of agents interact and update their opinions on some topic. I will first introduce an SDE model for a population with age structure, in which agents die and are replaced by new agents with random initial opinions. While this model displays the clustering that is typical in opinion dynamics, it is interesting that these clusters persist far beyond the lifetimes of individual agents. Such macroscopic patterns correspond to stationary distributions of the mean field PDE, arising in the large population limit. This talk will explain our approach to rigorously establishing the existence and properties of these stationary distributions.

Ramón Nartallo-Kaluarachchi (Oxford Mathematical Institute) | Nonequilibrium steady-states: Applications to brain and biology

Biological systems are characterised by their complexity. They are typically composed of heterogenous noisy interactions that violate symmetry and consume energy in order to facilitate a suite of functions. In this talk, I will focus on the mathematics of stochastic processes, in particular, nonequilibrium steady-states and stochastic thermodynamics. I will introduce this framework and illustrate how it can reveal insight into the thermodynamics of biological and neural systems. I will focus on results from my own work analysing neural data and will also present a number of new ideas that bring together statistical physics, stochastic dynamics, networks, discrete topology, and machine learning.