Title: Patterns in Multi-dimensional Permutations

Date: Thursday 2nd October 2025, 2.00pm

Venue: LT907

Abstract:  In this talk, I will present a general framework that extends the theory of permutation patterns to higher dimensions and unifies several combinatorial objects studied in the literature. The approach introduces the concept of a level for an element in a multi-dimensional permutation, which can be defined in various ways. I will discuss two natural definitions of level, each establishing connections to combinatorial sequences listed in the Online Encyclopedia of Integer Sequences (OEIS).

Our framework provides combinatorial interpretations for numerous OEIS sequences, many of which previously lacked such interpretations. As a notable example, we offer an elegant interpretation of the Springer numbers: they count weakly increasing 3-dimensional permutations under a level definition based on maximal entries.

This is joint work with Shaoshi Chen, Hanqian Fang, and Candice X.T. Zhang, all from the Chinese Academy of Sciences.

Title: Essential spectra of Sturm-Liouville operators and their indefinite counterpart

Date: Thursday 9th October 2025, 2.00pm

Venue: LT907

Abstract:  The difference to classical Sturm-Liouville expressions is here that the weight may change its sign.  In this situation the associated maximal operators become self-adjoint with respect to indefinite inner products and their spectral properties differ essentially from the Hilbert space situation.  We investigate the essential spectra and accumulation properties of nonreal and real discrete eigenvalues.

Title: Counting cycles in planar graphs

Date: Tuesday 21st October 2025, 3.00pm

Venue: LT907

Prof. Ryan Martin - Editor-in-Chief of the Journal "Order") https://faculty.sites.iastate.edu/rymartin/ 

Abstract: Basic Tur\'an theory asks how many edges a graph can have, given certain restrictions such as not having a large clique. A more generalized Tur\'an question asks how many copies of a fixed subgraph $H$ the graph can have, given certain restrictions. There has been a great deal of recent interest in the case where the restriction is planarity. In this talk, we will discuss some of the general results in the field, primarily the asymptotic value of ${\bf N}_{\mathcal P}(n,H)$, which denotes the maximum number of copies of $H$ in an $n$-vertex planar graph. In particular, we will focus on the case where $H$ is a cycle.

It was determined that ${\bf N}_{\mathcal P}(n,C_{2m})=(n/m)^m+o(n^m)$ for small values of $m$ by Cox and Martin and resolved for all $m$ by Lv, Gy\H{o}ri, He, Salia, Tompkins, and Zhu. The case of $H=C_{2m+1}$ is more difficult and it is conjectured that ${\bf N}_{\mathcal P}(n,C_{2m+1})=2m(n/m)^m+o(n^m)$. 

We will discuss recent progress on this problem, including verification of the conjecture in the case where $m=3$ and $m=4$ and a lemma which reduces the solution of this problem for any $m$ to a so-called ``maximum likelihood'' problem. The maximum likelihood problem is, in and of itself, an interesting question in random graph theory.

This is joint work with Emily Heath and Chris (Cox) Wells.

Title: A tractable framework for modelling hydrophilic large-swelling gels

Date: Wednesday 1st October 2025, 1.00pm

Venue: LT908

Abstract:  TBA

Title: Patient-Specific Multiscale Modelling of Glioblastoma: Targeted Modulation of
Interstitial Fluid Flow Using Electric Field

Date: Wednesday 15th October 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- The Lean Azimuthal Flame (LEAF) combustor concept: exhaust emissions and
flame topology

Date: Wednesday 29th October 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- TBC

Date: Wednesday 5th November 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- TBC

Date: Wednesday 26th November 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- TBC

Date: Wednesday 3rd December 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Multi-scale modelling of blood rheology in sickle cell disease

Date: Wednesday 28th January 2026, 1.00pm

Venue: LT907

Abstract:  Sickle cell disease (SCD) is a haematological disorder, caused by a genetic mutation, in which mutant haemoglobin molecules can polymerise under low-oxygen conditions, altering the biophysical properties of the red blood cells. These cell-level differences then result in changes in the whole-blood rheology -- and those rheological properties are in turn linked to the pathophysiology of SCD.  I use mathematical modelling and numerical simulation to develop descriptions of cell-cell interactions within blood flow, and validate these against experimental data.

Title: Modelling the Ouzo Effect: Making Clear Why Clear Things Go Cloudy

Date: Wednesday 11th February 2026, 1.00pm

Venue: LT907

Abstract:  Ouzo, and many other similar spirits such as pastis and sambuca, is clear when purchased in a bottle, but after adding a very small amount of water it becomes cloudy (it forms an emulsion).  This spontaneous emulsification is known as the ouzo effect, and is due to the trace amount of anise oil present in the drink.  It is of fundamental scientific interest, and also has a range of potential industrial applications.  One of the key open questions is why the emulsion is stable over very long timescales (months and years), despite requiring essentially no energy to form.

Title: TBA

Date: Wednesday 25th February 2026, 1.00pm

Venue: LT907

Abstract:  TBA

Title: TBA

Date: Wednesday 4th March 2026, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Multiscale modelling of soft tissue tearing

Date: Wednesday 18th March 2026, 1.00pm

Venue: LT907

Abstract:   We present a mathematically rigorous derivation of a multiscale elastic damage phase-field model using asymptotic homogenisation. Starting from the classical phase-field description of damage, we establish a consistent formulation satisfying the Karush–Kuhn–Tucker conditions, ensuring irreversibility of material degradation. The model is defined over an arbitrary composite domain of continua, enhancing general applicability. By applying asymptotic homogenisation under standard assumptions of local periodicity and smoothness, we derive a multiscale framework linking the microscopic structure to macroscopic behaviour. A detailed asymptotic expansion with respect to the scale separation parameter reveals a leading-order macroscale model free from microscale dependencies, with microscopic effects captured through a set of homogenised coefficients. Numerical simulations in one dimension confirm theoretical convergence of micro- and macroscale models as the scale separation parameter tends to zero. We further specify a representative microscale geometry and constitutive choices enabling simulation of aortic dissection, illustrating the framework’s applicability to complex soft tissue tearing phenomena. The proposed approach provides a mathematically sound and computationally efficient alternative to resolved microscale simulations.

Title: Fracture, by design: topology-programmed damage in Maxwell lattices

Date: Wednesday 25th March 2026, 1.00pm

Venue: LT907

Abstract:  Fracture is usually treated as an outcome to be avoided; here we see it as something we may write into a lattice's microstructure. Maxwell lattices sit at the edge of mechanical stability, where robust topological properties provide a way on how stress localises and delocalises across the structure with directional preference. Building on this, we propose a direct relationship between lattice topology and damage propagation. We identify a set of topology- and geometry-dependent parameters that gives a simple, predictive framework for nonideal Maxwell lattices and their damage processes. We will discuss how topological polarisation and domain walls steer and arrest damage in a repeatable way. Experiments confirm the theoretical predicted localisation and the resulting tuneable progression of damage and show how this control mechanism can be used to enhance dissipation and raise the apparent fracture energy.

Title: AI for Decision Support to Smallholder Farming

Date: Tuesday 16th September 2025, 2.00pm

Venue: LT908

Abstract:  Pests and diseases contribute to an estimated 40% reduction in global crop yields annually, necessitating scalable, data-driven interventions to help smallholder farmers. Recent advances in artificial intelligence (AI) and machine learning (ML) have demonstrated significant potential in augmenting plant health diagnostics and decision support at field scale. A forthcoming initiative, the AgriLLM Project, scheduled for launch at COP30 (Brazil, November 2025), integrates Large Language Models (LLMs) with CGIAR’s domain expertise to generate context-specific, real-time agronomic advisories. This innovation addresses persistent gaps in extension delivery across the Global South, where resource-constrained systems limit access to expert recommendations during critical phenological stages. Among operational examples, the Plantix platform has emerged as a large scale AI application, capable of automatically diagnosing over 700 biotic and abiotic stresses across 45 crops. Advisories were offered for over 150 million targeted farmer problems using plant damage symptoms. Complementing this, ICRISAT’s Plant Health Detector employs supervised ML classifiers to automate recognition of 50 crop damages. An Intelligent Agricultural Systems Advisory Tool is being piloted to provide climate smart pre-season and in-season advisories in India and Africa. Parallel advances in computer vision have facilitated spatially explicit tracking of invasive pests such as Spodoptera frugiperda, while citizen science models enhance surveillance. Collectively, these AI-enabled systems exemplify transformative pathways for resilient, data-driven crop advisory management for smallholders in Asia and Africa.

Title: Identifying the Effect of Climate Warming on Ocean Chlorophyll from Satellite Records Using Deep Learning and Multiple Regression Methods

Date: Wednesday 15th October 2025, 2.00pm

Venue: LT908

Abstract:  Anthropogenic climate warming is expected to cause long-term changes in global marine phytoplankton, affecting the marine ecosystem and fishery production. However, it is challenging to distinguish the effects of long-term climate warming from natural variability in satellite chlorophyll-a (Chl-a, a proxy for phytoplankton biomass) records—the only global-scale observations of phytoplankton—due to limited data length and interference from natural fluctuations. As a result, we still cannot confidently determine whether observed changes in global Chl-a from limited satellite records are caused by long-term climate warming or how quickly warming-induced Chl-a changes occur.

In this talk, I will introduce our recent two studies on this topic. First, we developed a deep learning model and trained it using an ensemble of Earth System Models’ simulations, which successfully detected the climate warming signal from satellite observations of global Chl-a. This validates the model’s predictions and highlights the emerging anthropogenic impacts on global marine phytoplankton. Second, using multiple regression models, we reduced the influence of natural climate variability on trend analysis of satellite Chl-a in the tropical Pacific from 1997–2023, revealing a long-term decline in Chl-a at a rate of about −4‰/yr. Global warming contributed to this decline at a rate of −14.5%/℃. This finding offers solid estimates of the global warming-driven trend in tropical marine phytoplankton biomass, helping to forecast future changes in marine ecosystems.

Title: Application of GAM in Exploring Long-Term Productivity-Biodiversity Relationships in Marine Microzooplankton Assemblages

Date: Wednesday 22nd October 2025, 2.00pm

Venue: LT511

Abstract:  Although the productivity-biodiversity relationship (PBR) has been a hot topic, few studies have considered how anthropogenic pressures affect PBRs in marine microzooplankton. Here, we applied GAMs to provide the first insights into PBRs in tintinnid assemblages using 18-year data from Jiaozhou Bay, a typical coastal bay in the Yellow Sea. We hypothesized and verified that PBRs vary across contrasting anthropogenic nutrient inputs and that functional and phylogenetic diversity would provide more information than conventional species richness. High productivity promotes higher diversity under low to medium rather than high anthropogenic nutrient inputs. Compared to species richness, functional and phylogenetic diversity reveal more PBR patterns and respond more rapidly to varying anthropogenic inputs. A concave PBR is revealed for functional diversity in the ecozone with highly active water exchange. Our study contributes to the understanding of PBRs in marine unicellular secondary producers and their responses to anthropogenic nutrient inputs in coastal ecosystems.

Title: Improving Predictions of Drug-Induced Arrhythmias Through Calibration of Action Potential Models

Date: Wednesday 26th November 2025, 2.00pm

Venue: LT907

Abstract:  

Mathematical action potential (AP) models describe changes in cell membrane voltage arising from a complex interplay of ionic currents and their potential interactions with drug compounds. These models can support preclinical risk assessment for drug-induced cardiac arrhythmias and help extract richer information from animal-based experiments. 

The rabbit Purkinje fiber assay is widely used in preclinical safety studies because it incorporates the major electrophysiological mechanisms present in human ventricular myocytes. When combined with such experiments, mathematical AP models can provide predictions of drug effects on cardiac electrophysiology. However, in practice, these models sometimes fail to reproduce key experimental findings.

This work aims to improve AP models by calibrating their parameters to reduce discrepancies between model predictions and experimental recordings. After introducing the modeling and calibration methods, preliminary results will be presented and discussed.

Title: Dissecting the Role of Phenotypic Variation in Cell Population Growth and Collective Self-Generated Chemotaxis

Date: Wednesday 28th January 2026, 2.00pm

Venue: LT907

Abstract:  Phenotypic variation is a ubiquitous feature of biological cell populations, even in genetically identical cells growing in uniform environments. Such variability can have profound consequences for population-level behaviour, particularly under stress, yet it is often neglected in classical modelling frameworks.

In the first part of this talk, I consider mathematical models of bacterial population growth that explicitly incorporate non-heritable variation in individual cell growth rates. I examine how phenotypic heterogeneity and environmental selection shape population growth and the dynamics of phenotypic subpopulations. We derive theoretical results for population growth rates and compare them with predictions from homogeneous models, identifying regimes in which variability qualitatively alters population outcomes. I also address the inverse problem of inferring distributions of generation times and phenotypic abundances from data, showing that competing model assumptions can be robustly distinguished.

In the second part of the talk, I turn to self-generated chemotaxis, a collective process in which cells modify their chemical environment to guide movement. Using a hybrid discrete–continuum model that couples stochastic cell motion with a continuum description of the chemoattractant, I investigate how phenotypic variation in motility, sensing, and chemical degradation affects the robustness of collective migration. Simulations and inference results highlight which sources of variability are most influential. The results and tools developed have broader implications for collective behaviour in cell biology, ecology, and evolution.

Title: Estimating the Role of Heterogeneities in the First-Wave Dynamics of COVID-19 in Nigeria

Date: Wednesday 18th February 2026, 2.00pm

Venue: LT907

Abstract:  Variation in individual specific traits such as susceptibility or connectivity that are not subject to short-term change can have an important influence on the dynamics and potential to control infectious disease outbreaks.  This is because the distribution of these heterogeneities in the susceptible population changes over the course of an outbreak as a result of higher levels of transmission between infected and more susceptible or more connected individuals, leading to additional reductions in the effective reproduction number and the herd immunity threshold.  This study estimates heterogeneities in susceptibility in COVID-19 transmission by fitting a modified Susceptible, Infected, Recovered model to incidence data. We estimate the parameters of the model using Markov Chain Monte Carlo techniques, first testing the proposed method on simulated data to determine its effectiveness before applying it to real-world incidence time-series from ten different Nigerian States supplied by their Centre for Disease Control. We estimated the average basic reproduction number to be 1.360 (95% credible intervals (CrI): 1.356 - 1.366), the heterogeneity parameter to be 2.561 (2.247 - 2.884), and the reporting probability to be 0.005 (0.004 - 0.006). Information criteria indicated that the model with heterogeneity was substantially better supported compared to an analogous homogenous model. The estimated value of the herd immunity threshold and final epidemic size with this estimated level of heterogeneity is much reduced compared to the homogenous model. Including heterogeneities in epidemiological models can improve the reliability of model-based inferences, and lead to better-informed decisions for managing emerging and re-emerging infectious disease outbreaks like COVID-19.

Title: On the Simultaneous Inference of Susceptibility Distributions and Intervention Effects from Epidemic Curves

Date: Wednesday 18th February 2026, 2.30pm

Venue: LT907

Abstract:  Individual variation in susceptibility lowers herd-immunity thresholds by causing highly susceptible individuals to be infected early, accelerating selective depletion of the susceptible pool. In Susceptible–Exposed–Infectious–Recovered (SEIR) models, this heterogeneity is often summarised by the coefficient of variation (ν). Although heterogeneity in susceptibility and non-pharmaceutical interventions (NPIs) act through different mechanisms—intrinsic depletion versus time-varying contact reduction—their effects on observed epidemic curves can be similar. As a result, when fitting a single epidemic trajectory, these components can lie along a pronounced “compensation ridge,” making them practically non-identifiable.

We first investigate this challenge using stochastic simulations of synthetic epidemics. Single-epidemic inference via maximum likelihood and Bayesian MCMC yields strong posterior/ likelihood dependence between ν and intervention strength c , with correspondingly wide uncertainty intervals. We then systematically study a multi-epidemic framework that jointly analyses 2–3 concurrent epidemics (e.g. distinct regions) under shared heterogeneity but differing initial conditions. This design reduces the ν–c dependence and improves numerical stability by roughly an order of magnitude, indicating that variation in timing and scale across epidemics can help constrain the trade-off.

We apply the approach to first-wave COVID-19 mortality in England and Scotland using a hierarchical Bayesian model with an explicit delay-based observation process. The real-data fits confirm that the compensation ridge persists: joint fitting narrows the feasible parameter region relative to single-country analyses, yet mortality data alone cannot fully disentangle depletion from lockdown effects without informative priors or additional information.

Finally, we study model misspecification: does the shape of the susceptibility distribution matter when mean and variance are matched? Comparing Gamma and Lognormal susceptibility models matched on moments, we find systematic differences driven by tail behaviour, with Gamma-based models tending to be structurally “optimistic” relative to Lognormal alternatives. Importantly, we show that although multi-epidemic fitting improves precision, it can also produce confident but biased inference when the susceptibility distribution is misspecified.

Title: An Adaptive Tolerance Selection Procedure to Avoid Local Minima in Approximate Bayesian Computation Algorithms for Use in Coagulation-Fragmentation Models of DNA Methylation

Date: Wednesday 25th February 2026, 2.00pm

Venue: LT907

Abstract:  DNA methylation is an important epigenetic phenomenon in which methyl groups are added to sequences of DNA. The addition of these chemical groups regulates gene expression and erroneous methylation patterns are associated with various diseases including Alzheimer’s and cancer. In this talk a mathematical model of DNA methylation will be developed using a coagulation-fragmentation based framework which allows the formation and break-up of clusters of methylated sites across DNA to be investigated. Model calibration requires the comparison of model output to experimental data to infer model parameters. However, for our model the likelihood function quickly becomes intractable as the system is scaled to realistically relevant lengths of DNA. This inability to obtain a likelihood is common when studying physically relevant systems and hence likelihood-free inference approaches are generally used. In this talk we will employ Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) for parameter estimation. ABC is a rejection sampling technique in which the intractable likelihood is replaced by a simulation-based comparison of experimental (observed) data and simulated data. The difference between experimental and observed data yields a discrepancy, with the accept/reject criteria then conditioned on whether this discrepancy is less than some tolerance value. We investigate the role of the tolerance sequence used in ABC SMC to iteratively navigate from a given prior distribution to posterior. Choosing a suitable tolerance sequence is critical to the success of ABC SMC and we have found that established techniques such as selecting pre-defined fixed tolerance sequences and adaptively updating tolerance selection as the inference process evolves can both result in the ABC SMC algorithm becoming trapped in regions of local minima in discrepancy space. Here we present an improved ABC SMC algorithm which overcomes this issue largely through simple sampling from the prior. The discrepancy associated with each parameter vector sampled from the prior is calculated and analysis of the corresponding discrepancy distribution using the Kneedle algorithm leads to the specification of an appropriate starting tolerance, allowing ABC SMC to begin near the global minimum in discrepancy space. Subsequent iterations of ABC SMC then refine inference around the true parameter values. We have found this new approach allows ABC SMC to infer parameter values reliably for systems which contain local minima in their discrepancy space. We will then move on to discuss some recent work related to the additional challenges faced when applying ABC SMC to stochastic systems of reaction networks. Firstly, it can be common for experimental data to consist of only a single realisation, which can then prove challenging to compare with stochastic models. We test the ability of the ABC SMC algorithm to successfully estimate parameter values when the simulated data consists of only single stochastic realisations of the model under investigation. We then conclude by investigating the role that stochastic statistics can play in improving parameter identifiability.

Title: The World’s Largest Macroalgal Bloom in the Yellow Sea, China: Formation and Implications

Date: Wednesday 4th March 2026, 2.00pm

Venue: LT907

Abstract:  The world’s largest trans-regional macroalgal blooms during 2008-2025 occurred in the Yellow Sea, China. A decadal study reveals the causes, development and future challenges in this unique case. Satellite imagery and field observations showed that the macroalgal blooms in the Yellow Sea originated from the coast of Jiangsu province and that favorable geographic and oceanographic conditions brought the green macroalgae from the coast offshore. Statistically, optimal temperature, light, nutrients and wind contributed to the formation and transport of the massive bloom north into the Yellow Sea and its deposition onshore along the coast of Shandong province. Certain biological traits of Ulva prolifera-efficient photosynthesis, rapid growth rates, high capacity for nutrient uptake, and diverse reproductive systems allowed growth of the original thousands of tonnes of biomass into more than one million tonnes of biomass in just two months. The proliferation of U. prolifera in the Yellow Sea resulted from a complex contingency of circumstances, including human activity (eutrophication by release of nutrients from wastewater, agriculture, and aquaculture), natural geographic and hydrodynamic conditions (current, wind) and the key organism’s biological attributes. Hydro-bio-model was developed for monitoring of green tide. Better understanding of the complex biological-chemical-physical interactions in coastal ecosystems and the development of an effective integrated coastal zone management with consideration of scientific, social and political implications are critical to solving the conflicts between human activity and nature.

Title: Fragility in a Togashi-Kaneko Stochastic Model with Mutations

Date: Wednesday 18th March 2026, 2.00pm

Venue: LT907

Abstract:  In biochemical and biological contexts, autocatalytic interactions—where a species promotes its own production, often through feedback with other components—play a central role. They capture nonlinear feedback loops capable of generating rich dynamical phenomena such as bistability, sustained oscillations, and stochastic switching between metastable states. These mechanisms are not only fundamental in chemical kinetics, where autocatalysis underlies classical models of pattern formation and self-organization, but they also appear in biological settings ranging from gene regulation and signalling cascades to ecological interactions and evolutionary dynamics. They have also appeared in models of opinion dynamics in populations. 

The Togashi-Kaneko (TK) stochastic model is a prototypical example of an autocatalytic reaction network exhibiting dramatic switching behaviour. The desire to understand this unusual behaviour has attracted considerable attention in recent years. In this talk, we study the TK model with additional mutations. We establish a rigorous stochastic averaging principle that describes slow dynamics in terms of certain ergodic means of fast variables. Beginning with two species, we demonstrate a sensitivity of the model to even slight departures from symmetry in the autocatalytic reactions. We accomplish this through a detailed analysis of the stationary distribution of the fast process when the state of the slow process is fixed. We call this high sensitivity property "fragility". We give some examples of behaviour that can occur when there are more than two species. These preliminary explorations for multiple species point to a wealth of open questions for future research. This is a joint work with Yi Fu, Hye-Won Kang, Lea Popovic, Greg Rempala, and Ruth J. Williams. (SIAM Journal of Life Sciences, in press)

Title: Prof. Daniel Appelö (Virginia Tech)

Date: Tuesday 27th January 2026, 3.00pm

Venue: Teams

Abstract: In this paper, we develop a framework for designing arbitrary high order low-rank schemes for the Lindblad equation with time-dependent Hamiltonians. Our approach is based on nested Picard iterative integrators (NPI) and results in schemes in Kraus form that are completely positive and trace preserving (CPTP). The schemes are amenable to low rank formulations, making them suitable for problems where the matrix rank of the density matrix is small.

Title:  Optimising Cold Chain Logistics for Net Zero: A Mathematical Framework for Policy Decision-Making

Date: Tuesday 24th February 2026, 3.00pm

Venue: LT908

Abstract: A zero-emissions cold chain (ZECC) involves transporting and storing temperature-sensitive goods, such as food and medicine, without emitting greenhouse gases. Cold-chain logistics present a rich class of combinatorial optimisation problems. In this talk, I will present a mathematical formulation for evaluating decarbonisation strategies in cold chain networks, with the view of informing UK policy. An example of such strategy is the use of alternative fuel types such as electric or hydrogen vehicles over diesel. The framework uses network flow models and vehicle routing problems to identify supply chain structures and optimal routing strategies while balancing competing objectives of minimising operational costs and carbon emissions. We will demonstrate this approach on real supply chain data from Scotland.

and

Title:  Using generative AI to accelerate uncertainty quantification in multiscale methods

Abstract: Multiscale methods, including both finite element and finite volume variants, are widely used to solve problems involving features across a broad range of scales. A classic example is the simulation of subsurface flow through porous media, such as the infiltration of gases in geological carbon storage. In such a setting, the rock permeability may vary on the micrometre scale, while the computational domain can span hundreds of metres or even kilometres. Multiscale methods allow fine-scale features to be resolved at a reduced cost by augmenting the approximation space using multiscale basis functions, which are typically obtained by solving relatively small local problems. In this talk, I will present work combining graph convolutional networks with denoising diffusion models and image super-resolution techniques to instead generate the multiscale basis functions conditionally on the problem parameters. This can reduce the overall computation time in the context of uncertainty quantification, where hundreds or thousands of simulations runs are performed.

Title:  Structure preserving methods for a class of nonlinear dispersive equations

Date: Tuesday 3rd March 2026, 3.00pm

Venue: LT908

Abstract:  In this talk we introduce a structure preserving, second order in time relaxation-type scheme for approximating solutions of a class of nonlinear dispersive equations. We highlight the advantages of such methods with regard to mimicking key properties of the continuous problems. We proceed to discuss the a posteriori error analysis of the scheme for the Schrödinger-Poisson system. In particular, we introduce an appropriate reconstruction and present the main steps of the derivation of optimal order a posteriori error bounds. The main challenges in the analysis arise from the nonlinear nature of the problem and the fact that the potential function, which is also unknown, is linked with the wave function. This means that we should obtain optimal a posteriori error estimates for both the potential and the wave function at the same time. Various numerical experiments verify and complement our findings.

Title:  Mixed-precision Computing: High Accuracy With Low Precision

Date: Tuesday 17th March 2026, 3.00pm

Venue: LT908

Abstract:  Mixed-precision algorithms have launched an era in which efficiency and accuracy are no longer mutually exclusive. Rather than rely entirely on high-precision formats like double (64-bit) precision, mixed-precision algorithms apply lower precisions such as single (32-bit) or half (16-bit) precision whenever possible, reserving higher precision only for critical steps. Doing so can drastically reduce memory requirements, improve performance, and lessen energy consumption on modern computer hardware without sacrificing accuracy or stability.

In this talk, we discuss the challenges of using low/mixed precision, and present five cases, common in scientific applications, where using mixed precision makes sense.

Date: Tuesday 24th March 2026, 3.00pm

Venue: LT908

Title:  Exploring Scalable Approaches to Network Alignment (CN)

Abstract: Network alignment is the task of identifying matching nodes in different networks. It is a useful tool in data analysis and arises in a wide range of applications, such as in aligning social networks. Despite its broad relevance, the problems on which it has been successfully used are limited in size since network alignment is NP-hard. For an approach that scales in size, one cannot hope to solve the problem exactly and good approximations may be preferred.

Title: Long time behavior for SIS model driven by pure-jump noise with Markov switching  

Wednesday 17th September 2025, 3.00-4.00pm

Venue: LT907

Abstract: In this talk, we focus on long time behavior for SIS epidemic model driven by an alpha-stable process with Markov switching. Necessary and sufficient conditions for recurrence or extinction have been established. To analyze the ergodic behavior, we establish the well-posedness of a nonlocal Dirichlet problem with regime switching and derive a strong maximum principle. Our results disclose heavy-tailed fluctuations and stationary distribution of Markov chain significantly affect the epidemic threshold and asymptotic dynamics. 

Title: A Journey Through Academia: Some Personal Reflections as a Member of an Under-Represented Group(s)

Date: Wednesday 29th October 2025, 3.00pm

Venue: LT908

Abstract: In this talk I will present a brief overview of my career, discussing why I became interested in Statistics and how this led to my academic journey to date, including several critical turning points. I will present personal reflections in the context of being an individual from an under-represented group through different career stages and in particular how this has subsequently informed how I now approach situations and in particular decision-making. I will also aim to discuss some aspects of leadership (at least as I see it) including some challenges this brings, as well as opportunities to support diversity.