Forum abstracts
Professor Raymond Goldstein, University of CambridgeLink opens in a new window
Non-equilibrium Economics
Equilibrium is a central concept both in statistical physics and in economics (although its meaning is somewhat different in the two cases). However, we know that for some (complex) physical systems, dynamics is ultra-slow and equilibrium is never reached. There are many reasons to assume that the complexity of real economies prevent them from reaching equilibrium, jeopardizing many of the predictions of standard macro-economic models (like the Dynamic Stochastic General Equilibrium – DSGE – model used by Central Banks). We will present several examples where plausible dynamics, describing the action of boundedly rational individuals, lead to economic situations that are permanently out-of-equilibrium, and fail to reach the optimal “nirvana” promised by classical economics.
Optical Engineering: Why Mathematical Insight—and AI—Matter
Engineering is fundamentally shaped by available technology, computational power, and mathematical insight. This talk explores how mathematical principles have driven key engineering breakthroughs and how artificial intelligence (AI) is now playing a transformative role in the field.
We begin with the evolution of Holographic Interferometry, a sophisticated optical technique that enables highly precise measurements. This method has provided valuable insights into optimizing combustion processes and, more recently, has shown promise in applications for CO₂ removal. When combined with the Minimum Spanning Tree (MST) algorithm, it becomes a powerful tool for mapping optical phase surfaces with high accuracy.
Recent advancements in holographic diffraction super-resolution microscopy—built upon many of the technologies discussed—have enabled researchers, with the assistance of AI, to capture the motion of nucleotides within the human nucleus in unprecedented detail. This progress addresses key questions regarding nucleotide motility. Furthermore, by combining 3D diffraction reconstructions with a polarized viewing system, the perception of 3D objects is significantly enhanced, and provides precise numerical measurement.
We also examine Particle Image Velocimetry (PIV), a technique that has transformed fluid dynamics by providing detailed flow visualization. From its early development using optical Fourier processing of photographic images to modern integrations of laser technology, digital imaging, correlation techniques, Principal Component Analysis, and tomography, PIV has enabled engineers to refine aerodynamic designs and optimize industrial processes. Additionally, it has been instrumental in tracking bacterial motility, providing deeper insights into microbial behaviour.
Throughout this talk, we emphasize the critical role of mathematical principles in driving engineering progress across diverse fields, from combustion diagnostics to medical applications. With AI and machine learning enhancing these capabilities, we are witnessing new levels of automation, accuracy, and predictive power - bridging the gap between theoretical models and real-world engineering challenges.
Responsive Hydrogels: Perspectives, Modelling, and Ongoing Efforts
Hydrogels are hydrophilic polymer scaffolds surrounded by adsorbed water. From a dry state, they are capable of swelling in volume by up to two orders of magnitude – becoming up to 99% water - whilst still remaining solid (if a little squishy).
Responsive hydrogels lose this affinity for water – sometimes quite profoundly – when an environmental stimulus, such as temperature or pH, transitions beyond a critical value. Conformational changes in the scaffold squeeze the water out, and the gel shrivels dramatically. This volume change is reversible (albeit non-reciprocal), allowing for cycles of activation and deactivation. As such, responsive hydrogels have important and exciting applications in microscale actuators.
This talk will describe some of the big open areas of hydrogel modelling, before detailing two (very) recent unpublished theoretical pieces of work on 1) thermoresponsive displacement pumps for microfluidic devices, and2) coupling pH-responsive gels to oscillating reactions to enable communication and synchronisation.
The edge of a cell: an active mechanosensing membrane
From neurons, plants, and elephant trunks to actuators and soft robots: The fascinating dynamics of smart active solids
The world around us, both natural and man-made, is filled with structures that respond to external stimuli and adapt their internal structures to perform specific functions. For example, plants have the ability to sense changes in their environment, such as changes in gravity or light, and modify their shape accordingly to survive. During development, neurons respond to their surroundings by growing and connecting different parts of the brain. Additionally, the arms of octopuses and trunks of elephants are stunning examples of the versatility and beauty of responsive structures that inspire the development of soft robotics. In the field of engineering, liquid crystal elastomers can be designed to respond to light or heat, offering exciting opportunities for new devices and actuators. In this talk we will first develop a general theory of smart active solids, characterized by a solid matrix with embedded active components and suitable to model both biological and physical systems. I will propose a unified mathematical framework to model how multiple stimuli can be combined at the microscopic level to produce changes at the macroscopic level. This framework will allow us to uncover general principles for microstructure organization and activation for filamentary systems. Furthermore, the feedback loop created by shape-shifting in response to external sources can produce complex dynamics similar to natural behaviors, providing elegant solutions to functional problems.
Alain Gorierly- http://goriely.com/
Silent Spreaders: Behaviour and Equilibrium Under Asymptomatic Infection
This paper analyzes equilibrium social distancing choices in a model with potentially asymptomatic infection. Since infection only prompts symptoms probabilistically, individuals cannot perfectly infer their health state from the absence of symptoms. Instead, they must form beliefs about their health state based on knowledge of the population frequencies. I show that relative to a benchmark with perfect health state information, asymptomatic infection leads to lower mitigation through four distinct channels, some mechanistic and some that work through beliefs and thus decisions. The model is then applied to an analysis of individual and mass testing. The value of the former derives from the value of information and it is shown that the latter may influence the course of the epidemic through its influence on aggregate equilibrium behaviour. Tests for immunity generally have a higher value of information and aggregate effects than tests for infection.
Prof Flavio Toxvaerd- https://sites.google.com/site/toxvaerd11/
Regime shifts, species extinctions and long transients in ecological models
Predicting critical transitions and regime shifts in ecosystems has long been a key point of biological conservation and ecological management. Traditionally, mathematical models of regime shifts focused on long-term, asymptotic behaviour of ecosystems. Therefore, critical transitions were expected to occur in the case of lack of system’s resilience, as a quick response to changes in environmental parameters. However, recently, an alternative scenario of regime shifts in ecological models has been explored, which accounts for long-term transient dynamics. According to the transient-based scenario, there may be no underlying proximal source of a regime shift, or alteration of the system could have occurred long ago before the transition. Despite the recognition of the importance of long transients in ecology, and more generally, in environmental sciences, this research area seems to be at its early stage in terms of lack of systematic studies, developing appropriate research techniques, and linking theory to practical applications. In this talk, I briefly review main types of transient regimes occurring in deterministic and stochastic mathematical models. A special attention is paid to exploring patterns of long transients occurring in time-discrete population models with discontinuous (piece-wise) maps. In those systems, long transients are due to crawl-by dynamics, chaotic repellers, chaotic saddles, ghost attractors, and various intermittent regimes. There is also possibility of cascades of transient regimes. A major conclusion is that the discontinuity of the map in population dynamics models significantly facilitates the emergence of long transients by creating new types and increasing parameter domains with transient dynamics. Overall, the omnipresence of long transients in mathematical models, supported by data, emphasizes the need to account for them in long-term forecasting and crisis anticipation.
Leveraging replication in active learning
Many time-consuming simulators exhibit a complex noise structure that depends on the inputs. Advances in Gaussian process modeling with input-dependent noise, especially via replication (iid repetitions of the same experiment), allow efficient modeling with better uncertainty quantification on the predictions. We focus here on strategies for balancing replication, exploitation and exploration for various sequential design goals, possibly in parallel batches. These goals include global model accuracy, optimization, contour finding, and dimension reduction. Illustration on synthetic examples are provided as well as a large scale massively parallel real world epidemiology problem.
Dr Mickael Binois https://sites.google.com/site/mickaelbinoishomepage/
From Substrate Dissipation to Internal Dissipation Dynamics in Cell-Level Tissue Modelling: Generating Sustained Flows
Complex tissue flows in epithelia are driven by intra- and inter-cellular processes that generate, maintain, and coordinate mechanical forces. There has been growing evidence that cell shape anisotropy, manifested as nematic order, plays an important role in this process. Here we extend a cell-resolution active nematic model by replacing the usually assumed substrate friction with internal viscous dissipation, dominant in epithelia not supported by a substrate or the extracellular matrix, which are found in many early-stage embryos. When coupled to cell shape anisotropy, the internal viscous dissipation allows for long-range velocity correlations and thus enables the spontaneous emergence of flows with a large degree of spatiotemporal organisation. We demonstrate sustained flow in epithelial sheets confined to a channel, providing a link between the cell-level and continuum active nematic theories. Our findings also show a simple mechanism that could account for collective cell migration correlated over distances large compared to the cell size, as observed during morphogenesis.
Jan Rozman - https://www.physics.ox.ac.uk/our-people/rozmanLink opens in a new window
If you are interested in meeting with the speaker, please contact Gareth Alexander.
AI Decision-Making: Insights from Adversarial Attacks and High-Dimensional Analysis
Despite a revolution in artificial intelligence, the technology is not yet integrated into critical systems due to a lack of trust. Ensuring the robustness and reliability of artificial neural networks (ANNs) is not just necessary but imperative to the full utilisation of the technology. This talk investigates model robustness and the adversarial vulnerabilities that compromise ANNs, emphasizing that no single approach can solve all problems. We will explore high-dimensional feature space analysis as a way to understand decision making, critically examining its potential and limitations in enhancing model reliability. Designed to cater to a technically diverse audience, this presentation aims to provide an accessible overview of the current state of research, highlighting both advancements and the significant hurdles that remain. It will also discuss the broader implications of these challenges, underscoring the importance of Advai’s ongoing efforts in developing reliable AI systems.
Epidemiology & modelling of zoonotic arbo-pathogens in the One Health framework: insights from case studies
Vector-borne and zoonotic pathogens have complex epidemiological cycles at the interface between vertebrate hosts (animals and humans), vectors and the environment. Examples of such pathogens include Rift Valley fever (RVF), West Nile and Crimean-Congo Haemorragic fever (CCHF) viruses, which are widespread around the globe with limited disease control options. These diseases generate concern both in terms of their potential geographical expansion to disease-free areas and potential expansion of their host range. Outbreaks are sparingly documented and rarely quantified, demanding the formal assessment of disease control measures or projection of disease spread.
Here, by combining field data collection and mathematical modelling, I will present the on-going work on RVF, CCHF and other tick-borne zoonoses from temperate areas, and discuss how these works can be used or improved to assist disease control and prevention. I would like to acknowledge all my collaborators on these studies, allowing such an interdisciplinary work to be implemented.
Wages and Capital returns in a generalized Pólya urn
It is a widely observed phenomenon that wealth is distributed significantly more unequal than wages. We study this phenomenon using an extension of Pólya’s urn, modelling wealth growth through wages and capital returns. We focus in particular on the role of increasing return rates on capital, which have been identified as a main driver of inequality. Labor share is the second main parameter of our model. We fit the parameters from real-world data in Germany, so that simulation results reproduce the empirical wealth distribution and recent dynamics quite accurately, and are essentially independent of initial conditions. Our model is simple enough to allow for a detailed mathematical analysis and provides interesting predictions for future developments and on the importance of wages and capital returns for wealth aggregation.
This is joint work with Thomas Gottfried (Augsburg).
A spectrum of physics-informed machine learning approaches for structural health monitoring
Structural health monitoring as a research field began by studying physical models for systems, comparing modelled and monitored response to understand observed behaviours. As sensing technology advanced and monitoring data became more readily available, many practitioners and researchers adopted a purely data-driven approach capitalising also on technology enhancement from the machine learning field. Today, where we would like to be able automatically assess the health of our structures across their operational envelope, we find that despite these advances, we often lack data that represent all behaviours of interest, thereby precluding an entirely data-driven approach.
Via a Gaussian process framework, this talk will present a number of ways of incorporating the physical insight we will often have of the structure we are attempting to model or assess into a machine learning approach as a potential solution to this problem. The talk will demonstrate how grey-box models, that combine simple physics-based models with data-driven ones, can improve predictive capability for structural assessment and system identification tasks. A particular strength of the approaches demonstrated here is the capacity of the models to generalise, with enhanced predictive capability in different regimes, increasing applicability in light of the aforementioned challenge.
Lizzy Cross is Head of the Department of Mechanical Engineering and a Professor in the Dynamics Research Group at the University of Sheffield with a research focus on advanced data analysis and machine learning for structural health monitoring (SHM) and nonlinear system identification. After an undergraduate in Mathematics she switched to Mechanical Engineering for MSc and PhD. She has just completed an EPSRC Innovation Fellowship pioneering physics-informed machine learning for structural dynamics. Lizzy is a co-director of the Laboratory for Verification and Validation, a state-of-the-art dynamic testing facility (lvv.ac.uk). She has published over 140 articles, including 45 journal papers, 6 invited book chapters. She was recently awarded the Achenbach medal which recognises an individual (within 10 years of PhD) who has made an outstanding contribution to the advancement of the field of SHM.
Synchronization in collectively moving inanimate and living active matter
Collective motion, found in flocking birds, swarming insects or bacterial colonies, arises from local interactions between entities and entails individuals to unify their velocity. The underlying mechanism that enables this process remains elusive. Focusing on a limited number of cells in simple circular confinements we identify waves of polymerizing actin that function as pacemaker governing the speed of individual cells. We show that the onset of collective motion coincides with the synchronization of the nucleation frequencies of newly emerging waves of polymerizing actin across the population. Employing a simpler and more readily accessible mechanical model system of active spheres we identify the same key requirement to reach the analogous collective state, i.e. the synchronization of the individuals' internal oscillators. The mechanical 'toy' experiment illustrates that the global synchronous state is achieved by nearest neighbor coupling. We suggest by analogy that local coupling and the synchronization of actin waves are essential for emergent, self-organized motion of cell collectives.
From PhD to startup founder: A journey in mathematical entrepreneurship
In this talk, I will be sharing my experience from the last year, transitioning from an applied maths PhD into founding a fluid simulation startup. I'll start by giving a background to my academic research during my time as a PhD student, and how the skills I gained led me to founding my startup, VanellusLink opens in a new window, alongside my co-founder Laurence CullenLink opens in a new window. I'll then go through what we do at Vanellus, where we're working to build a new commercial computational fluid dynamics (CFD) package, that leverages recent machine learning research to achieve order-of-magnitude efficiency gains. I'll finish off the talk by sharing advice on what industry and entrepreneurship has to offer for maths PhD graduates, and how you can get into it yourself.
Multi-scale free-boundary problems (and case studies in industrial maths)
Many continuum mechanics problems of practical and industrial interest are difficult to model or simulate accurately due to the multiple spatial scales involved. Additional complexities arise when there are also free or moving boundaries. I will discuss two examples of this that I have worked on recently, both motivated by real world/industrial problems: (i) the chemical decontamination of a porous building material (for instance, following a chemical weapons attack) and (ii) the motion of an evaporation front through a drying filter membrane. In each case we used asymptotic analysis (combining homogenisation and boundary layer analyses) to derive much simpler, computationally tractable continuum PDE models, which give new insight into the process for industrial partners. As well as the mathematics and modelling, I will talk briefly about my experiences collaborating with industry and government throughout my PhD and recent postdoc.
Scalable inference for epidemic models with individual data
As individual level epidemiological and pathogen genetic data become available in ever increasing quantities, the task of analysing such data becomes more and more challenging. Inferences for this type of data are complicated by the fact that the data are usually incomplete, in the sense that the times of acquiring and clearing infection are not directly observed, making the evaluation of the model likelihood intractable. A solution to this problem can be given in the Bayesian framework with unobserved data being imputed within Markov chain Monte Carlo (MCMC) algorithms, at the cost of considerable extra computational effort. Motivated by this demand, we describe a novel method for updating individual level infection states within MCMC algorithms that respects the dependence structure inherent within epidemic data.
Panel discussion: Academia - beyond the PhD
After a brief introduction, this Q&A session will attempt to address any questions you may have about the academic career pathway, from applications and interviews to the day-to-day of navigating the responsibilities of the role. It is a dynamic environment that has changed considerably in past years (and is likely to do so again in the future), with the discussion aiming to provide a range of opinions on the profession at the present point in time.
Kat Does Maths – from bouncing droplets to Subathons
Outside of my PhD research I spend my time doing maths communication under the name “KatDoesMaths”, and what better way to describe this talk. It’ll be broken down into three sections: The first “Kat”, I’ll talk you through my journey to get to where I am. “Does” is where I want to share with you all the tips and tricks of the Maths Communication trade that I’ve picked up over the years, and how to get started if you’re thinking about getting into outreach. And then finally I’m going to actually communicate some “Maths” with you. It’s a bold statement to talk about my ability to communicate maths and then follow it up with a research talk, but I’m excited to share with you my work (and there are some pretty pictures at the end!). Away from the glitz and glam of Maths Communication, I study fluid dynamics. In order for a droplet to rebound from a liquid surface there needs to be an air layer keeping the two liquids separate, else the droplet will coalesce. My research is investigating the dynamics of this air layer, and I will introduce my numerical model which is able to recreate this system.
Nature’s ‘sixth’ sense? – sensing electrical fields in the natural world
Recently, bees and spiders (and other such arthropods) have been shown to detect electrical fields (a sense known as electroreception). Whilst this novel discovery expands our view of how such organisms explore their environment, exactly how this curious sense works remains unclear. With such new discoveries, many questions arise that require both theoretical and empirical examination. In this talk, I will introduce the fundamentals of this sensory phenomena, and uncover some of the mechanical and sensory complexities that have been discovered through mathematical studies.
I will explain the mechanics of electroreception via sensory hairs and show the physical and biological feasibility of this sense. From this basis, I will delve deeper into the interactions between hairs and electrical fields introducing the concept of a sensitivity contour (regions of the solution space where hairs deflect to a given sensory threshold), and examine how a hair’s sensory capability changes owing to electrical interactions between hairs. Finally, I will discuss some of the sensory possibilities of electroreception (e.g. object identification and location detection) and the biological implications of this (e.g. foraging decisions, predator-prey behaviour).
Wednesday 07 June 2023: Dr Lucia Marucci (Bristol)
Programming cells using feedback control and whole-cell modelling
The ability to program and design ad hoc cellular and biological processes offers exciting opportunities in basic research, in the biotechnology industry and in the clinic.
Difficulties in engineering cellular phenotypes robust to changes and perturbations, as well as the lack of established tools to design biological functions across scales, still represent major roadblocks.
In this talk I will start discussing our recent research that leverages feedback control to engineer robust cellular phenotypes. I will show results obtained using intracellular, external or multicellular controllers in both bacterial and mammalian cells, and new applications of Cybergenetics methodologies we are currently exploring (e.g., control-based analysis of gene networks’ dynamics, combination therapy design).
I will also mention a complementary approach aimed at rational and computer-aided cell design via whole-cell models (WCMs), which are mathematical models designed to capture the function of all genes and multiscale processes within a cell. The design of minimal bacterial genomes will be used as a proof-of-concept; I will also show how machine learning can support WCMs’ output interpretation and solve their computational burden challenge.
Our tools and results should make the design and control of complex cellular phenotypes and laboratory engineering a step closer.
Title: Using data science to understand society and support policy makers
Abstract: The rapid increase in data availability has opened up novel opportunities for policy makers to design policies based on better, more up-to-date data about the state of society. In this talk, I will first discuss how data and data science can be used to better understand our society, such as measuring travel flows or estimating the English index of multiple deprivation. I will then share my experience of collaborating with a range of UK policy makers, such as the data science team in 10 Downing Street, and discuss how research can be co-designed to deliver impact.
Data and analytics in public health: the world beyond Covid-19
This talk will describe the UK’s experience of Covid-19 by examining the analytical and policy developments over the last few years. The vast amounts of data available during the pandemic posed challenges as well as opportunities for those at the heart of government. Harnessing those data, and presenting key insights in the right way, was key to many of the public health interventions the UK successfully deployed. Many of the lessons from Covid-19 have also left a lasting impact on the UK’s approach to public health, including the response to Mpox in 2022 and Avian Flu in 2023. It’s an exciting time for anyone interested in health analytics and the talk will conclude with some of the latest work UKHSA is doing to build capabilities for everyday and emergency public health threats.
Gonorrhoea modelling – AMR and vaccination
Katy will present a range of approaches to modelling gonorrhoea from simple to complex and discuss how modelling can inform public health policy decisions.
Gonorrhoea is the only STI on the WHO list of high priority pathogens where AMR is a concern. STIs are still stigmatised and management is often in specialist settings (GUM) clinics. Gonorrhoea is rare at the population level and concentrated in key populations and infections are often asymptomatic. Katy will discuss how we can incorporate these clinical, behavioural and management aspects in models and how these also play into to public policy and decision making.
Dry inertial active matter
Active particles which are self-propelled by converting energy into mechanical motion represent an expanding research realm in physics and chemistry. For micron-sized particles moving in a liquid (``microswimmers''), most of the basic features have been described by using the model of overdamped active Brownian motion [1]. However, for macroscopic particles or microparticles moving in a gas, inertial effects become relevant such that the dynamics is underdamped. This introduces the new active matter class of dry inertial active matter. Recently, active particles with inertia have been described by extending the active Brownian motion model to active Langevin dynamics which includes inertia [2]. In this talk, recent developments of active particles with inertia are summarized including: inertial delay effects between particle velocity and self-propulsion direction [3], the influence of inertia on motility-induced phase separation [4] leading to a refrigerator []5, Langevin rockets [6] and the formation of active micelles (“rotelles”) by using inertial active surfactants [7].
References
[1] C. Bechinger, R. di Leonardo, H. Löwen, C. Reichhardt, G. Volpe, G. Volpe, Reviews of Modern Physics 88, 045006 (2016).
[2] H. Löwen, Journal of Chemical Physics 152, 040901 (2020).
[3] C. Scholz, S. Jahanshahi, A. Ldov, H. Löwen, Nature Communications 9, 5156 (2018).
[4] S. Mandal, B. Liebchen, H. Löwen, Physical Review Letters 123, 228001 (2019).
[5] L. Hecht, S. Mandal, H. Löwen, B. Liebchen, Physical Review Letters 129, 178001 (2022).
[6] A. R. Sprenger, S. Jahanshahi, A. V. Ivlev, H. Löwen, Physical Review E 103, 042601 (2021).
[7] C. Scholz, A. Ldov, T. Pöschel, M. Engel, H. Löwen, Science Advances 7, eabf8998 (2021).
Wednesday 23 November 2022: Dr Stephen Parnell (Warwick)
Detection and control of invasive plant pests and diseases; an epidemiological modelling approach
Plants absorb cardon dioxide, provide us with food, and deliver a wide range of other ecosystem services crucial for life. Introductions of exotic plant pests and diseases have increased sharply in recent times threatening this. Prominent recent examples include Ash Dieback Disease, which has been estimated to destroy 90% of ash trees in UK following its first discovery in 2012. Detection and management of pests and diseases across vast and complex host landscapes is challenging. Models allow us to capture current epidemiological understanding and identify strategies to detect and control epidemics. In this seminar, drawing on a range of examples, I will demonstrate how epidemiological models have been used to address a wide range of practical questions from: where and when should surveillance be conducted? To, how should stakeholders be engaged with to maximise the success of a disease control policy?
Dr Kat Rock (Warwick) - in person
Epidemiological modelling for elimination
In this presentation I will highlight how mechanistic modelling and health economic analyses are being used to support the elimination of gambiense human African trypanosomiasis (gHAT, African sleeping sickness) – targeted for elimination of transmission by 2030. gHAT is a vector-borne infection, transmitted by tsetse in Central and West Africa. I shall present work of the HAT modelling and economic predictions for policy (HAT MEPP) project, which focuses on using regional, longitudinal human case data to parameterise location-specific models and provide tailored recommendations for strategies aimed at elimination. I will overview how data from other sources – including tsetse monitoring and cost collection – has contributed to these analyses and impacted our recommendations for policy makers. During the presentation I will demonstrate the HAT MEPP graphical user interface that was developed in conjunction with national programmes to support dissemination of modelling results in an accessible way for non-modellers. Finally I will discuss the challenges of attempting to select cost-effective strategies for infections targeted for elimination.
Wednesday 09 November 2022: Dr Edson Utazi (WorldPop, School of Geography and Environmental Science, University of Southampton)
Bayesian hierarchical modelling approaches for combining information from multiple sources to produce annual estimates of national immunization coverage
Estimates of national immunization coverage are crucial for guiding policy and decision-making in national immunization programs and setting the global immunization agenda. WHO and UNICEF estimates of national immunization coverage (WUENIC) are produced annually for various vaccine-dose combinations and all WHO Member States using information from multiple data sources and a deterministic computational logic approach. The WUENIC approach employs ad hoc estimation rules to supplement administrative and official coverage data reported to WHO and UNICEF with coverage estimates obtained from nationally representative household surveys. The approach, however, is incapable of characterizing the uncertainties inherent in coverage measurement and estimation. It also provides no statistically principled way of exploiting and accounting for the interdependence in immunization coverage data collected for multiple vaccines, countries and time points. In this talk, I will present novel Bayesian hierarchical modelling approaches for producing accurate estimates of national immunization coverage and associated uncertainties. I will discuss two candidate models that we proposed: a balanced data single likelihood (BDSL) model and an irregular data multiple likelihood (IDML) model, both of which differ in their handling of missing data and characterization of the uncertainties associated with the multiple input data sources. I will present a simulation study that demonstrates a high degree of accuracy of the estimates produced by the proposed models, and which also shows that the IDML model is the better model. I will then show the results obtained from an application of the methodology to produce coverage estimates for select vaccine-dose combinations for the period 2000-2020. The utility and reproducibility of the methodology are enhanced by a contributed R package ‘imcover’ implementing the No-U-Turn Sampler (NUTS) in the Stan programming language.
Prof Mark van Rossum (Nottingham)
Energy efficient learning with in neural networks
The brain is one of the most energy intense organs. Some of this energy is used for neural information processing, however, fruitfly experiments have shown that also synaptic plasticity is metabolically costly. First we will present estimates of this cost, introduce a general model of this cost, and compare it to costs in computers. Next, we turn to a supervised artificial network setting and explore a number of strategies that can save energy need for plasticity, either by modifying the cost function, by restricting plasticity, or by using less costly transient forms of plasticity. Finally, we will discuss adaptive strategies and possible relevance for computer hardware.
Wednesday 19 October 2022: Dr Adam Noel (School of Engineering, Warwick)
Molecular Communication Engineering: Signal Propagation and Information at the Microscale
Molecular communication is an emerging field that applies communication engineering tools to molecular signalling. My talk will focus on introducing molecular communication from the context of communication engineering. I will then briefly present an on-going project where we seek to understand and control signalling in microscale biological systems. The case studies for this project are competitive dynamics in bacteria and glucose regulation in organ-on-a-chip systems.
Wed 22 June 2022
Merve Alanyali (Head of Data Science Academic Partnerships and Research, LV= General Insurance)
From Warwick to LV= General Insurance: A tale of a data scientist
As a Warwick alum, in this talk I will be covering how my career has changed across years starting from the postgraduate days and how my background is helping me with my current position at LV= General Insurance. I will be covering main pillars of our data science team; “delivery” by showcasing example projects, “technical excellence” by giving example of our team best practices including delivery framework to Python packages we open sourced, and “research and innovation” by talking about our academic partnerships and research projects ending with a discussion around the future of data science as well as academia-industry collaborations.
Wednesday 08 June 2022 - Nikolai Bode (Bristol) (virtual)
Smart swarms or mindless mobs: social behaviour in pedestrian crowds at different spatio-temporal scales
The movement dynamics of pedestrian crowds are a paradigmatic example for emergent behaviour, where the interactions between many individuals give rise to the system’s behaviour. It seems natural to ask if this mode of operation functions well or not. In this talk, I will try to convince you that pedestrian crowds are neither particularly smart, nor entirely mindless by taking you for a walk through our patchy understanding of pedestrian social behaviour. In my research I am especially interested in relating theoretical models to empirical data. So, you can expect to see a range of models being used in attempts to explain different data sets.
Wednesday 25 May 2022 - Shreyas Mandre (Warwick) (in person)
Functional interpretation for transverse arches of the human foot
The fossil record indicates that the emergence of arches in human ancestral feet coincided with a transition from an arboreal to a terrestrial lifestyle. Propulsive forces exerted during walking and running load the foot under bending, which is distinct from those experienced during arboreal locomotion. I will present mathematical models with varying levels of detail to illustrate a simple function of the transverse arch. Just as we curve a dollar bill in the transverse direction to stiffen it while inserting it in a vending machine, the transverse arch of the human foot stiffens it for bending deformations. A fundamental interplay of geometry and mechanics underlies this stiffening -- curvature couples the soft out-of-plane bending mode to the stiff in-plane stretching deformation. This result overturns a century-old theory that the longitudinal arch underlies the bending rigidity of the human foot. In addition to presenting a functional interpretation of the transverse arch of the foot, this study also furnishes an interpretation of the fossil record implying that the evolution of the transverse arch may have preceded the longitudinal arch.
Wednesday 11 May 2022 - Rebecca Killick (Lancaster)
Tackling Global Issues: Change
Traditional statistical model building assumes that the same model (and fitted parameters) can describe the data at any point in the observed process. To tackle early violations to this statisticians introduced regressors to describe time-varying features such as trend and seasonality. These have enabled model building to become engrained in everyday applications across all fields. But what happens when this neat assumption of a static model is no longer appropriate?
The simplest departure from this static model assumption is to piece together static models, which we understand well. Questions then surface around where and how many of these static models should we use? This field of statistical modelling is called changepoint detection.
This talk will give a potted history of the challenges in changepoint detection that I have been preoccupied with over the last ten or so years. Always motivated by applications of changepoint detection to real-world problems, the tour of statistical challenges will be littered with applications to different areas including environment, health and business. To conclude the talk I will describe some of the existing challenges that must be addressed by the community if changepoint detection is going to remain relevant to the world.
Within the talk, my aim is to convey the intuition behind the field, its challenges and proposed solutions. As such, the talk will be accessible to a general audience.
Wednesday 16 March 2022 - Nicole Nisbett (Leeds)
Unearthing climate action discourses with sentence transformers
In this talk I explain how we use natural language processing and the BERTopic algorithm to uncover topics from text. The algorithm uses a transformers-based topic modeling approach, embedding models, clustering, and vector space representations to provide meaning to unstructured text data. We apply these models to large volumes of Twitter data on the climate crisis discussion around the UN Climate Change Conferences (COP) to understand the core themes in the public debate and how these have changed between 2014 and 2021.
Wednesday 09 March 2022 - Yonatan Berman (KCL)
A Simplified Mortality Multiplier Method: New Estimates of Wealth Concentration
We derive a method for estimating top wealth shares using estate tax data and minimal data on mortality. It is a simplification of the mortality multiplier method. We first investigate the conditions in which the novel method provides accurate wealth concentration estimates and validate it empirically. We then use it to produce novel long-run historical series of the distribution of wealth for Belgium, Japan, and South Africa, where data have not been exploited yet. This is particularly important for historical inequality studies, given that existing series of wealth inequality are very scarce and only cover a few developed countries.
This is joint work with Facundo Alvaredo and Salvatore Morelli
Wednesday 16 February 2022 - Simon Schnyder (Kyoto)
Epidemics of infectious diseases posing a serious risk to human health have occurred throughout history. During the ongoing SARS-CoV-2 epidemic there has been much debate about policy, including how and when to impose restrictions on behavior. Under such circumstances policymakers must balance a complex spectrum of objectives, suggesting a need for quantitative tools. Here we show how costly interventions, such as taxes or subsidies on behaviour, can be used to exactly align individuals' decision making with government preferences even when these are not aligned. We assume that choices made by individuals give rise to Nash equilibrium behavior. We focus on a situation in which the capacity of the healthcare system to treat patients is limited and identify conditions under which the disease dynamics respect the capacity limit. In particular we find an extremely sharp drop in peak infections as the maximum infection cost in the government's objective function is increased. This is in marked contrast to the gradual reduction without government intervention. The infection costs at which this switch occurs depend on how costly the intervention is to the government. We find optimal interventions that are quite different to the case when interventions are cost-free.
Wednesday 09 February 2022 - Tommaso Lorenzi (Politecnico di Torino)
Dissecting the evolutionary and spatial dynamics of cancer through partial differential equations
A range of mathematical models have been used to gain a more in-depth theoretical understanding of different aspects of cancer dynamics. In this talk, deterministic, continuum models formulated as partial differential equations will be considered. The first part of the talk will focus on partial integro-differential equations modelling the eco-evolutionary dynamics of cancer cells in vascularised tumours. In the second part of the talk, attention will turn to models of avascular tumour growth that comprise coupled systems of nonlinear partial differential equations, which reflect the heterogeneous cellular composition of the tumour micro-environment. Analytical and numerical results summarising the behaviour of the solutions to the model equations will be presented and the biological insight generated by these results will be discussed.
Wednesday 19 January 2022 - Diana Khoromskaia (Crick Institute)
Active morphogenesis of patterned epithelial shells
Shape transformations of epithelial tissues in three dimensions, which are crucial for embryonic development or in vitro organoid growth, can result from active forces generated within the cytoskeleton of the epithelial cells. How the interplay of local differential tensions with tissue geometry and with external forces results in tissue-scale morphogenesis remains an open question. Here, we describe epithelial sheets as active viscoelastic surfaces and study their deformation under patterned internal tensions and bending moments. In addition to isotropic effects, we take into account nematic alignment in the plane of the tissue, which gives rise to shape-dependent, anisotropic active tensions and bending moments. We present phase diagrams of the mechanical equilibrium shapes of pre-patterned closed shells and explore their dynamical deformations. Our results show that a combination of nematic alignment and gradients in internal tensions and bending moments is sufficient to reproduce basic building blocks of epithelial morphogenesis, including fold formation, budding, neck formation, flattening, and tubulation.
Preprint: D. Khoromskaia and G. Salbreux, arXiv:2111.12820 [physics.bio-ph]
Wednesday 12 January 2022: Sophie Meakin (London School of Hygiene and Tropical Medicine, and MathSys alumna)
Comparative assessment of methods for short-term forecasts of COVID-19 admissions in England at the local level
Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.
Wednesday 08 December 2021 - Rosanna Barnard (London School of Hygiene and Tropical Medicine)
Modelling SARS-CoV-2 transmission in England: vaccines, variants and variability
In this talk I will present mathematical modelling work considering the dynamics of SARS-CoV-2 transmission in England. I will discuss the challenge of modelling disease transmission within an ever-evolving epidemiological context, with consideration of COVID-19 vaccinations, variants of concern, and various uncertainties such as behavioural changes. I will outline the compartmental modelling framework we use to describe SARS-CoV-2 transmission, as well as the data sources and the model fitting processes used. I will give an overview of various modelling outputs we have produced which have informed the UK response.
Wednesday 01 December 2021 - David SibleyLink opens in a new windowLink opens in a new window (Loughborough)
Coupled dynamics of fluid systems with mass transfer
In this talk we will consider how to develop models for systems of fluids that involve complex behaviour such as mass transfer. We will consider multiphase systems of a single component in solid, liquid and vapour form, and systems that involve both fluids and colloids. In both situations the dynamics should account for conserved behaviour such as surface tension driven flow, and non-conserved behaviour due to evaporation/condensation. We will discuss results for the solid-liquid-vapour system in detail. We consider a regime where a droplet of liquid spreads over a solid and in the presence of a vapour, all of the same material (such as ice-water-water vapour). We consider dynamics where vapour can condense to liquid which in turn can freeze to solid throughout a droplet spreading process, and indeed vice-versa. By developing a thin-film model capturing phase-change, surface tension, density contrast, and interfacial potentials, a rich phase diagram will be explored---and complex dynamics such as layered solid growth driven by forces at the contact lines described [work in collaboration with Pablo Llombart (Universidad Complutense Madrid), Eva G. Noya (CSIC Madrid), Andrew J. Archer, and Luis G. MacDowell (Universidad Complutense Madrid); based on Nat. Commun. 12, 239 (2021)].
Wednesday 24 November 2021 - Alejandra Avelos Pacheco (Harvard)
Multi-study Bayesian factor regression analysis for heterogenous high-dimensional biological data
Two key challenges in modern statistical applications are the large amount of information recorded per individual, and that such data are often not collected all at once but in batches. These batch effects can be complex, causing distortions in both mean and variance. We propose a novel sparse latent factor regression model to integrate such heterogeneous data. The model provides a tool for data exploration via dimensionality reduction and sparse low-rank covariance estimation while correcting for a range of batch effects. We study the use of several sparse priors (local and non-local) to learn the dimension of the latent factors. We provide a flexible methodology for sparse factor regression which is not limited to data with batch effects. Our model is fitted in a deterministic fashion by means of an EM algorithm for which we derive closed-form updates, contributing a novel scalable algorithm for non-local priors of interest beyond the immediate scope of this paper. We present several examples, with a focus on bioinformatics applications. Our results show an increase in the accuracy of the dimensionality reduction, with non-local priors substantially improving the reconstruction of factor cardinality. The results of our analyses illustrate how failing to properly account for batch effects can result in unreliable inference. Our model provides a novel approach to latent factor regression that balances sparsity with sensitivity in scenarios both with and without batch effects and is highly computationally efficient; and opens new avenues for future research on dimension-reduction-based data integration.
Wednesday 17 November 2021 - Katerina Kaouri (Cardiff)
Modelling across scales: from fertilization and embryogenesis to COVID-19 modelling
I will present an overview of some interdisciplinary challenges I have been working on, in collaboration with experimentalists and industry. I will start with the important societal challenge of improving In-Vitro Fertilization (IVF) therapies, proceed with models of calcium signalling in IVF and in embryogenesis. I will also relay our work on modelling airborne transmission of COVID-19 in indoor spaces which has been developing over the last year in collaboration with Oxford University and the Welsh government.
A geometric lens to reveal links between structure and dynamics of networks
Describing networks geometrically is a powerful tool to understand the link between structure and signal propagation as well as in the design of efficient learning algorithms. Traditionally, geometric descriptions involved embedding nodes in low-dimensional manifolds or normed vector spaces. However, an incompatible embedding space can lead to detrimental information loss. In this talk, I will generalise the Ollivier-Ricci edge curvature by quantifying the interaction of a pair of dynamical processes. Crucially, this definition does not require embedding and induces geometric representations at different resolutions through the time parameter of the dynamics, which can unfold the multiscale structure of arbitrary networks. To show this, I will illustrate that the edge curvature unveils deep connections between network structure and signal propagation. Specifically, a single quantity, the edge curvature, bounds the rate of information flow across the edge, revealing network bottlenecks. To capture these bottlenecks, I will introduce a geometric generalisation of network modularity, a quality function that encodes clusters based on the curvature weighted graph without requiring a statistical null model. I will then prove that for sparse stochastic block model networks geometric modularity is optimal in the sense that it recovers clusters until the well-known phase transition of detectability. Finally, I will demonstrate how geometric modularity is useful to capture clusters based on dynamic interactions in real-world networks, including the European power grid and a dataset of C. elegans single neurons gene expressions.
Wednesday 03 November 2021 - Ruth Baker, University of Oxford
Teasing apart the impact of electric fields on single-cell motilityCell motility in response to environmental cues forms the basis of many developmental processes in multicellular organisms. One such environmental cue is an electric field, which induces a form of motility known as electrotaxis. Electrotaxis has evolved in a number of cell types to guide wound healing, and has been associated with different cellular processes, suggesting that observed electrotactic behaviour is likely a combination of multiple distinct effects arising from the presence of an electric field. In order to determine the different mechanisms by which observed electrotactic behaviours emerge, and thus to design electric fields that can be applied to direct and control electrotaxis, we require accurate quantitative predictions of cellular responses to externally-applied fields. Here, we use mathematical modelling to formulate and parametrise a variety of hypothetical descriptions of how cell motility may change in response to an electric field. We calibrate our model to observed data using synthetic likelihoods and Bayesian sequential learning techniques, and demonstrate that electric fields impact cellular motility in three distinct ways.
Wednesday 20 October 2021 - Gabriela Gomes, University of Strathclyde
Frailty variation in population dynamics: Adventures and misadventures of an elusive concept
Selection acting on unmeasured individual variation is a common source of bias in the analysis of populations. It has been shown to affect measured rates of mortality1-3, the survival of endangered species4,5, the scope of neutral theories of biodiversity and molecular evolution6,7, the measured risk of diseases whether non-communicable8,9 or infections10-15, and the efficacy of interventions such as vaccines16-20 or symbionts21,22. Building on this knowledge, we have addressed how selection on individual variation might have affected the course of the coronavirus disease (COVID-19) pandemic23.
This form of variation that responds to selection and impacts within-cohort population dynamics, termed frailty variation by Vaupel et al. (1979)2, constitutes a most genuine phenomenon that scientific disciplines have been dismissing for decades. I will present some examples and discuss the mixed attitudes towards what is arguably the most elusive concept in population dynamics.
References:
1. Keyfitz, N. & Littman, G. (1979) Popul. Stud. 33, 333-342.
2. Vaupel, J., Manton, K. & Stallard, E. (1979) Demography 16, 439-454.
3. Vaupel, J., & Yashin, A. (1985) Am. Stat. 39, 176-185.
4. Kendall, B. E. & Fox, G. A. (2002) Conserv. Biol. 16, 109-116.
5. Jenouvrier, S, Aubry, L. M., Barbraud, C, Weimerskirch, H & Caswell, H. (2018) J. Anim. Ecol. 87, 212-222.
6. Steiner, U. K. & Tuljapurkar, S. (2012) Proc. Natl. Acad. Sci U. S. A. 109, 4684-4689.
7. Gomes, M. G. M., King, J. G., Nunes, A., Colegrave, N. & Hoffmann, A. (2019) Ecol. Evol. 16, 8995-9004.
8. Aalen, O. O., Valberg, M., Grotmol, T. & Tretli, S. (2015) Int. J. Epidemiol. 4, 1408-1421
9. Stensrud, M. J. & Valberg, M. (2017) Nat. Commun. 8, 1165.
10. Anderson, R. M., Medley, G. F., May, R. M. & Johnson, A. M. (1986) IMA J. Math. Appl. Med. Biol. 3, 229-263.
11. Dwyer, G., Elkinton, J. S. & Buonaccorsi, J. P. (1997) Am. Nat. 150, 685-707.
12. Smith, D. L., Dushoff, J., Snow, R. W. & Hay, S. I. (2005) Nature 438, 492-495.
13. Bellan, S. E., Dushoff, J., Galvani, A. P. & Meyers, L. A. (2015) PLOS Med. 12, e1001801.
14. Gomes, M. G. M., et al. (2019) Nat. Commun. 10, 2480.
15. Corder, R. M., Ferreira, M. U. & Gomes, M. G. M. (2020) PLOS Comput. Biol. 16, e1007377.
16. Halloran, M. E., Longini, I. M. Jr. & Struchiner, C. J. (1996) Am. J. Epidemiol. 144, 83-97.
17. O’Hagan, J. J., Hernán, M. A., Walensky, R. P. & Lipsitch, M. (2012) AIDS 26, 123.
18. Gomes, M. G. M., et al. (2014) PLOS Pathog. 10, e1003849.
19. Gomes, M. G. M., Gordon, S. B. & Lalloo, D. G. (2016) Vaccine 34, 3007.
20. Langwig, K. E., et al. (2017) mBio 8, e00796-17.
21. Pessoa, D., et al. (2016) PLOS Comput. Biol. 10, e1003773.
22. King, J. G., Souto-Maior, C., Sartori, L. M., Maciel-de-Freitas, R. & Gomes, M. G. M. (2018) Nat. Commun. 9, 1-8.
23. Gomes, M. G. M., et al. (2020) medRvix 10.1101/2020.04.27.20081893.
Wednesday 13 October 2021 - Bruce Edmonds (Manchester Met)
Staging abstraction from a complex model of voter turnout
Simpler models (e.g. mathematical models that are analytically tractable) can rigorously determine the overall behaviour of a model, but the approximations and assumptions necessary to make these solvable can make the connection to what is being modelled weak. Complicated simulation models can more directly represent observed processes (as they are understood), i.e. are relevant, but are difficult to rigorously understand. Thus, it is increasingly common to use a combination of simulation and analytical models when trying to understand complex systems. This talk discusses some work which extends and formalises this approach, staging the abstraction into a sequence of models, starting with a complicated, descriptive model (that represents) but then progressively simplifying this into 3 more stages with an analytical model at the 'top'. This attempts to obtain both rigour and relevance of the modelling but at the cost of a more complicated 'pile' of models and a lot more work. The particular target that this work focussed on was "why people bother to go out and vote", which was part of a collaborative grant between the department of Theoretical Physics and the Cathie Marsh Centre (for quantitative social science) at the University of Manchester, with us modellers, at the Centre for Policy Modelling, Manchester Met. University being the bridge between the two.