The aim of the conference is to showcase the work done by the students in the Statistical Applied Mathematics at Bath (SAMBa) CDT, which encompasses research at the interface of statistics, probability, numerical analysis and applied mathematics. The conference is organised by the student cohort and will display our work to the rest of the department, mathematicians from other universities, and our industrial partners. As such, the program will be primarily devoted to talks and poster presentations from SAMBa students, complemented by invited talks from three experienced researchers from academia and industry.
If you have any questions about the conference, you're welcome to email the organisers Ben Robinson and Adwaye Rambojun.
Thanks to all who attended and helped to make the first SAMBa Summer Conference a success!
Take a look at the photo gallery from the event.
Below is the finalised programme for the conference.
Tuesday 20th June (PDF)  Wednesday 21st June (PDF)  

9:00  9:30  Coffee  
9:30  10:00  Session 3  Networks & Applied Probability Sponsored by BT Student Talk  Anna Senkevich Student Talk  Sam Moore Invited Talk  Anja Sturm 

10:00  10:30  
10:30  11:00  
11:00  11:30  Coffee  Coffee 
11:30  12:00  Session 1  Uncertainty Quantification Sponsored by Schlumberger Student Talk  Owen Pembery Student Talk  Marcus Kaiser Invited Talk  Colin Morice 
Session 4  Short Student Talks Followed by Coffee & Discussion 
12:00  12:30  
12:30  13:00  
13:00  13:30  Lunch  Lunch 
13:30  14:00  
14:00  14:30  Session 2  Mathematics & Statistics in Health Sponsored by AstraZeneca Student Talk  Matt Thomas Student Talk  Robbie Peck Invited Talk  Richie Gill 
Session 5  Applied Mathematical Modelling Sponsored by GKN Student Talk  Kate Powers Student Talk  Beth Boulton Close 
14:30  15:00  
15:00  15:30  
15:30  16:00  Coffee  
16:00  16:30  Poster Session  
16:30  17:00  
17:00  Drinks Reception  
18:30  Dinner 
As the global population ages, musculoskeletal diseases are becoming a major healthcare burden. The most common and widespread musculoskeletal disease is osteoarthritis (OA). OA of the joints of the lower limb, in particular the hip and the knee, have significant negative burden on the overall health state of an individual. The importance of activity for overall health state has become apparent, thus joint disease not only carries a burden in terms of pain and disability of the affected joint but also carries a further burden by increasing the risk of systemic conditions such as diabetes and cardiac disease due to the imposed reduction in activity. OA of the knee is increasing in prevalence driven by the ageing of the global population and is associated with obesity. This increase in knee disease is driving demand for knee replacement; a demand that healthcare systems are already struggling to meet. This talk will describe the development of a subject specific treatment for early OA of the knee aimed at preserving the natural joint and delaying the need for joint replacement. Part of the methodology being employed in the development process is an in silico clinical trial; the opportunities of using such methods for reducing the development time of novel therapies will be discussed.
This talk will describe methods for merging historical observational data to reconstruct past air temperatures across the globe from the mid19th century, based on satellite and terrestrial air temperature observations over land, ocean, lakes and ice. These methods are being developed as part of the EUSTACE project, which aims to produce daily estimates of near surface air temperatures over the whole Earth at high spatial resolution, with quantified uncertainties.
The talk will begin with a short overview of the EUSTACE project, describing the steps that it is taking to produce a climate dataset that is fit for a wide range of applications. The talk will then focus on the methods for merging observational data into the global analysis. A statistical model will be described that decomposes air temperature variability into variability at different spatial and temporal scales, permitting estimation of air temperature fields from sparse observational data. A model for observational biases and uncertainty is included to account for the heterogeneous observing network, which will also be described. Preliminary examples of application of the analysis system will be shown.
Stochastic interacting particle systems describe the time evolution of particle systems that are distributed on a discrete space (such as the integer lattice). The random changes to these systems are local, and one of the main points of interest is to understand the global and longtime behaviour that arises from a particular set of local interaction rules.
In this talk, we will discuss properties such as longtime survival and the existence of (nontrivial) invariant laws for a particular model called the cooperative branching coalescent. Here, particles perform independent random walks, only pairs of particles occupying neighbouring sites can produce new particles (cooperative branching) and particles that land on an occupied site merge with the particle present on that site (coalescence). Depending on the underlying space this system undergoes a phase transition as the branching rate is increased: For small branching rates the so called upper invariant law is trivial and the process started with finitely many particles ends up with a single particle with probability 1. Both statements are not true for high branching rates.
The talk is based on joint work with Jan Swart (UTIA Prag) and work in progress with Tibor Mach (University of Göttingen).
Macroparasites are complicated little blighters which cause infections which, due to the unconventional life cycles of parasites and the importance of variation in the severity of infection, can't be modeled like your standard bout of plague. This talk will explain why the classic SIS and SIR modelling falls apart when faced with macroparasitic infections and introduce an alternative method of modelling them, and why these models are important.
In many applications one is interested in sampling from probability distributions known up to a multiplicative constant. A standard technique to estimate such distributions are Markov Chain Monte Carlo (MCMC) methods, like Metropolis Hastings, where one generates a large number of uncorrelated samples of the distribution.
An important question is the rate at which these samples convergence to the steady state. We can think of this as the efficiency of the sampling method, which has a direct influence on the computational cost. To improve this efficiency, it is important to gain a better understanding of the involved stochastic processes.
On that account, we investigate in this talk the question why irreversible processes tend to converge faster to equilibrium than reversible ones.
Recent work in the physics literature has explored the twospecies contact process as a model of staged infections. The work has a biological interpretation in terms of hostparasite invasions, for example, when a growing colony of bacteria is under threat from a developing bacteriophage infection. The problem has previously been explored from a deterministic angle, in the setting of the lattice.
In this talk we shall begin by introducing the twospecies contact process in generality before moving on to suggest a novel stochastic approach to its analysis using a branching model. In particular we aim to show how a generalisation of the branching process, with a multi type offspring distribution, may be adopted to describe the evolution of the secondary wave of infection directly.
"Should I take my umbrella with me today?"
Many problems one encounters in life involve making decisions, including ones about the evidence you might want to collect to help inform further decisions. Making optimal decisions may depend on unknown parameter values, about which one may hold particular degrees of belief. To make these optimal decisions, we need to be able to formulate the problem clearly, and then apply mathematical reasoning and optimisation.
The Bayesian Decision Theory approach is based on quantifying the tradeoffs between various decisions using current beliefs in probability and the costs that accompany such decisions. At the very least, the approach can be considered as making common sense deductions which are validated by rigorous mathematics by framing a problem in probabilistic terms.
After introducing the motivating examples and theory, the application to experimental design is briefly discussed, where Bayesian Decision Theory is used to help identify, and build sufficient evidence of the efficacy of, treatments that may be used to treat medical conditions.
Uncertainty Quantification (UQ) for acoustics problems is valuable for improving safety in areas such as seismic imaging.
However, finding numerical solutions to the Helmholtz equation (the simplest model of acoustic wave propagation) is a very demanding computational task.
If we want to discover properties of sound waves moving in random media we may need to solve the Helmholtz equation thousands of times, which is computationally prohibitive, unless we can find methods which speed up our computations.
One approach to reducing the computational time needed to solve these equations is to use a preconditioner, which transforms our hardtosolve problem into one that is easier to solve.
In this talk we will introduce current work on preconditioning for the Helmholtz equation in random media, motivated by the study of sound waves moving through random media.
In particular, we will show how, rather than needing to calculate thousands of preconditioners when we need to solve the Helmholtz equation thousands of times, in certain situations we can calculate one preconditioner, which we can then use every time we solve the Helmholtz equation, which leads to computational savings.
Nearly all modern vehicle turbochargers are formed of a centrifugal compressor and turbine. Understanding how turbochargers act under various flow regimes is important for many reasons, including improving vehicle fuel efficiency. Current modelling of turbocharger performance is predominantly via CFD, which fails to capture system dynamics near unstable flow regimes.In this talk I will describe a more analytical method for modelling turbocharger compressors. I will formulate Euler equations in this setting and use averaging techniques to reduce the system to a 1dimensional description of the flow. Finally, I will present results for the steadystate case.
This talk will describe the asymptotic behaviour of the reinforced branching processes with fitness on the example of the preferential attachment tree of Bianconi and Barabasi. In this random graph model a popularity of a node is determined by its degree and a socalled fitness value, drawn from a specified distribution. The dynamics of these networks depend on the properties of the distribution, leading to three distinct behaviours, namely noncondensation phase, and extensive and nonextensive condensation phases. Such model provides a useful tool for understanding the growth characteristics of complex networks such as the Internet, social networks and online communities.
Calculating the burden of disease attributed to air pollution requires accurate estimation of population level exposures to pollutants. Although coverage of ground monitoring networks is increasing, these data are insufficient to independently estimate exposures globally. Information from other sources, such as satellite retrievals, chemical transport models and land use covariates must therefore be used in combination with ground monitoring data. Each of these data sources will have their own biases and uncertainties that may vary over space. Set within a Bayesian hierarchical modelling framework, the recently developed Data Integration Model for Air Quality (DIMAQ) integrates data from multiple sources and allows spatiallyvarying relationships between ground measurements and other factors that estimate fine particulate matter (PM2.5) concentrations. The outputs of the model are estimated exposures that can be combined with population estimates to produce populationlevel distributions of exposures for each country. DIMAQ was used to estimate exposures of PM2.5, together with associated measures of uncertainty, on a highresolution grid (~11 km × 11 km) covering the entire globe for use in the 2016 WHO report ‘Ambient air pollution: A global assessment of exposure and burden of disease’, and in the 2015 and 2016 updates of the Global Burden of Disease.
For 2015, 92% of the world’s population lived in areas that exceeded the WHO 10 µg/m3 guideline. Fifty percent of the global population resided in areas with PM2.5 concentrations above the WHO Interim Target 1 (IT1 of 35 µg/m3 ); 64% lived in areas exceeding IT2 (25 µg/m3 ); and 81% lived in areas exceeding IT3 (15 µg/m3 ). Nearly all (86%) of the most extreme concentrations (above 75 µg/m3 ) were experienced by populations in China, India, Pakistan, and Bangladesh.
The organisers would like to thank our industrial sponsors who helped to make this event possible. Thanks go to:
AstraZeneca, for sponsoring our session on Mathematics & Statistics in Health;
BT, for sponsoring our session on Networks & Applied Probability;
GKN, for sponsoring our session on Applied Mathematical Modelling;
Schlumberger, for sponsoring our session on Uncertainty Quantification.
This conference is also supported by the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa), under the project EP/L015684/1.
The conference will be held in the Wolfson Lecture Theatre (4 West 1.7), in the Department of Mathematics (marked as 4W on this campus map).
The address is:
Department of Mathematics
4 West
University of Bath
Claverton Down
Bath
BA2 7AY
See the university's guide on how to find us.
If you are arriving by train, you should arrive at Bath Spa train stations. You can then take the U1, 17 or 18 bus to the university from Dorchester Street (opposite the station to the left), or walk to the university in 3040 minutes (up quite a steep hill!).
Coming from Bristol airport, take the A4 bus to Bath, which brings you to the train station, from where you can walk or take a bus, as described above.