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University of Bath

Lead supervisors for the Centre for Doctoral Training in Statistical Applied Mathematics

We are academic staff from the Department of Mathematical Sciences who may be available to supervise your PhD project as your lead supervisor.

Get in touch with us about PhD projects.

    Ben Adams

    • Mathematical biology
    • Infectious disease epidemiology and evolution
    • Ecological modelling

    Karim Anaya-Izquierdo

    • Statistical geometry
    • Spatial analysis
    • Survival analysis and statistical methods in epidemiology

    Jonathan Bartlett

    • Methods for handling missing data in statistical analyses
    • Causal inference methods for randomised trials
    • Biostatistical and epidemiological applications

    Ruth Bowness

    • Mathematical modelling to explore medical and biological problems
    • Infectious disease dynamics
    • Differential equations and Individual-based modelling

    Tatiana Bubba

    • Inverse problems (in particular, tomography)
    • Sparse regularisation and optimisation (in particular, wavelet and shearlets)
    • Machine learning and deep learning in imaging

    Chris Budd

    • Industrial applied maths especially problems involving electricity, food or telecommunications.
    • Numerical weather forecasting and data assimilation.
    • Non smooth dynamical systems, friction, impact and chaos.

    Kirill Cherednichenko

    • Scale-interaction phenomena via asymptotic analysis of PDE
    • Operator theory and functional models
    • Applied calculus of variations

    Alex Cox

    • Probability and applications in Mathematical Finance
    • Stochastic optimal control
    • Martingale optimal transport

    Jonathan Dawes

    • Dynamical systems (pattern formation, reaction-diffusion problems, bifurcation theory)
    • Networks and dynamics
    • Fluid mechanics (nonlinear phenomena, asymptotic methods)

    Manuel del Pino

    • Analysis of nonlinear partial differential equations
    • Blow-up patterns in nonlinear evolution problems
    • Singular limits in variational problems with loss of compactness

    Evangelos Evangelou

    • Generalised Linear Models: Modelling, Approximate Methods, Value of Information
    • Spatial and Spatial-Temporal Geostatistics: Modelling, Sampling Design
    • Time Series: Modelling, Sequential Analysis

    Sergey Dolgov

    • Numerical linear algebra and scientific computing
    • Approximation and reduction of multivariate functions and tensors
    • Probabilistic and quantum modelling

    Matthias Ehrhardt

    • Inverse problems (e.g. models, algorithms)
    • Large-scale, randomized optimization (e.g. convergence guarantees, rates)
    • Applications (e.g. imaging, machine learning, deep learning

    Jonathan Evans

    • Asymptotic analysis and perturbation methods
    • Industrial and applied mathematical modelling
    • Complex fluids with memory, high order nonlinear evolutionary PDEs and free boundary problems

    Julian Faraway

    • Functional data analysis
    • Shape Statistics
    • Applications of Statistics

    Veronique Fischer

    • Harmonic Analysis (commutative and non-commutative)
    • Lie groups, homogeneous domains, representation theory
    • Pseudo-differential operators and Partial Differential Equations

    Silvia Gazzola

    • Inverse problems and regularization
    • Image restoration and reconstruction
    • Numerical linear algebra, Krylov subspace methods

    Ivan Graham

    • Analysis and solvers for high frequency wave problems
    • PDEs with random input data and UQ
    • PDE eigenvalue problems and reactor stability

    Kari Heine

    • Sequential Monte Carlo and Markov Chain Monte Carlo methods
    • Martingales and Markov processes
    • Computational methods in population genetics

    Antal Járai

    • Models arising from statistical physics, with an emphasis on understanding critical phenomena
    • Abelian sandpile model of self-organised criticality
    • Behaviour of random walks on fractal graphs

    Chris Jennison

    • Complex stochastic models
    • Markov Chain Monte Carlo samplers
    • Adaptive and group sequential clinical trials

    Daniel Kious

    • Reinforced random walks, self-interacting processes
    • Random walks in random environment, or in dynamical environment
    • Reinforcement learning

    Lisa Maria Kreusser

    • Dynamical systems and partial differential equations (modelling, analysis and numerical analysis)
    • Data analysis and mathematical approaches to machine learning
    • Applications in biology, climate science, engineering and industry

    Andreas Kyprianou

    • Self-similar processes, Lévy processes and their applications
    • Spatial branching, fragmentation and coalescing processes
    • Stochastic (numerical) modelling

    Cécile Mailler

    • Branching processes
    • Pólya's urns and stochastic approximation
    • Random networks

    Karsten Matthies

    • Averaging and Homogenisation for PDEs
    • Infinite-dimensional dynamics: PDEs and lattice ODEs
    • Many particle dynamics

    Paul Milewski

    • Geophysical fluid mechanics and conservation laws
    • Nonlinear waves and free-surface problems
    • Mathematical biology

    Eike Müller

    • Scientific computing, HPC and novel architectures
    • Fast solvers for partial differential equations in atmospheric fluid dynamics
    • Algorithms and software for stochastic differential equations and molecular dynamics

    Monica Musso

    • Partial differential equations and nonlinear analysis
    • Concentration phenomena in nonlinear elliptic equations
    • Blow-up in nonlinear parabolic equations

    Matt Nunes

    • Wavelets and lifting schemes
    • Time series, image and network analysis
    • Bayesian Computation

    Mark Opmeer

    • Model reduction
    • Control theory
    • Analysis

    Marcel Ortgiese

    • Stochastic analysis with applications in biology
    • Random networks
    • Stochastic processes in random environment

    Sarah Penington

    • Probabilistic models motivated by population genetics
    • Spatial branching processes with interactions
    • Applications of probability theory to partial differential equations

    Mathew Penrose

    • Pure and applied probability
    • Stochastic Geometry
    • Random graphs, percolation and interacting particle systems

    Andrea Pizzoferrato

    • Clustering algorithm
    • Application of inverse problems techniques
    • Neural Networks

    Clarice Poon

    • Inverse problems and compressed sensing
    • Machine learning and optimisation
    • Infinite dimensional regularisation

    Tristan Pryer

    • Numerical methods for geophysical fluid problems
    • Natural Disasters
    • Automated computational adaptive algorithms

    Matt Roberts

    • Probability
    • Branching processes: branching Brownian motion, branching random walks
    • Random graphs, random environments

    Tim Rogers

    • Graphs and networks
    • Applied stochastic processes
    • Emergent phenomena

    Christian Rohrbeck

    • Extreme value theory
    • Methods for spatial and spatio-temporal data
    • Bayesian computation

    Sandipan Roy

    • Statistical analysis of networks and graphical models
    • High dimensional inference
    • Optimization Methods

    Hartmut Schwetlick

    • Analysis, Partial differential equations, and Applied mathematics
    • Modelling of biological systems and Numerics
    • Geometric analysis

    Deborshee Sen

    • Scalable Bayesian methods (including Markov chain Monte Carlo algorithms).
    • Particle filtering and sequential Monte Carlo algorithms.
    • Bayesian neural networks.

    Tony Shardlow

    • Stochastic PDEs and their applications
    • Langevin equations
    • Numerical methods for strong and weak approximation

    Simon Shaw

    • Bayesian networks and uses of conditional independence
    • Bayes linear methods
    • Analysis of collections of (second-order) exchangeable sequences

    Pranav Singh

    • Design and analysis of numerical methods for PDEs
    • Geometric numerical integration
    • Optimal design of quantum systems (NMR, laser, quantum gates)

    Jey Sivaloganathan

    • Variational problems
    • Applied analysis, Partial differential equations
    • Nonlinear elasticity, fluid mechanics

    Theresa Smith

    • Methods for spatial and spatio-temporal data
    • Computation for Bayesian methods
    • Applications in the public health and the social sciences

    Euan Spence

    • Propagation of acoustic and electromagnetic waves
    • Transform methods for linear and nonlinear integrable PDEs
    • Problems at the interface between analysis and numerical analysis of PDEs

    Philippe Trinh

    • Asymptotic analysis and perturbation theory
    • Industrial and applied mathematical modelling
    • Fluid dynamics and free-surface flows

    Hendrik Weber

    • Stochastic partial differential equations
    • Rough path theory and regularity structures
    • Statistical mechanics

    Miles Wheeler

    • Nonlinear partial differential equations
    • Free boundary problems in fluid mechanics
    • Local and global bifurcation theory

    Jane White

    • Using mathematical models to explore problems in healthcare
    • Non-invasive drug monitoring and infectious disease control
    • Behaviours of network systems

    Kit Yates

    • Mathematical modelling of biological systems in which stochasticity plays an important role
    • Efficient stochastic modelling and simulation methodologies
    • A range of biological application areas: (e.g. cell migration, embryogenesis, Collective animal behaviour, parasite dynamics, pattern formation)

    Luca Zanetti

    • Unsupervised and semi-supervised learning on graphs
    • Spectral graph theory
    • Randomised algorithms and Markov chains