Staff

Find out more about the SAMBa management team and potential PhD supervisors

Staff

Find out more about the SAMBa management team and potential PhD supervisors

SAMBa Management

Find out more about the members of the SAMBa Management Team who manage the Centre on a daily basis

Alex Cox

Co-Director

Professor Alex Cox is Co-Director of SAMBa and Deputy Head of the Department of Mathematical Sciences. Alex is interested in research questions arising in probability and their applications in mathematical finance.

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Lindsay Melling

Operations Officer

Lindsay Melling is involved in the operational aspects of the SAMBa CDT, including responsibility for the oversight, delivery and development of Integrative Think Tanks, partner liaison, marketing and dissemination.

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Lou Adkin

Centre Coordinator

Lou Adkin delivers the administrative support for SAMBa, oversees the admissions process, and supports programme delivery.

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Marcel Ortgiese

Executive Team

Dr Marcel Ortgiese is a senior lecturer in the Department of Mathematical Sciences. Marcel’s research interests are in probability theory, in particular in stochastic systems that involve a spatial or network component.

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Matt Nunes

Executive Team

Matt Nunes is a Professor in the Department of Mathematical Sciences. Matt is interested in the applications of multiscale methods and wavelets to various areas of statistics, such as the analysis of non-uniformly sampled data and images. 

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Matthias Ehrhardt

Executive Team

Dr Matthias Ehrhardt is a Reader in the Department of Mathematical Sciences and is interested in the mathematics of data science, particularly the interface of optimization, inverse problems and machine learning.

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Philippe Trinh

Executive Team

Dr Philippe Trinh is a lecturer in applied mathematics. Phil’s research is motivated by a range of physical applications in the applied sciences, with particular emphasis on fluid mechanics and classical hydrodynamics.

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Silvia Gazzola

Executive Team

Dr Silvia Gazzola is a senior lecturer in the Department of Mathematical Sciences. Silvia is interested in regularization methods for inverse problems, with a particular focus on numerical linear algebra techniques and imaging applications.

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Susie Douglas

Co-Director

Dr Susie Douglas oversees the strategic direction and operation of the SAMBa CDT. Susie works across University CDTs to add value to the research and training portfolio.

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Theresa Smith

Deputy Director

Dr Theresa Smith is a senior lecturer in the Statistics Group in the Department of Mathematical Sciences. Theresa’s research is in the areas of spatial-temporal models and computation for Bayesian methods.

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Tim Rogers

Executive team

Professor Tim Rogers is interested in understanding and predicting the behaviour of complicated random events and processes, in particular when there are network or spatial structures involved.

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Lead Supervisors

Find out more about the academic staff from the Department of Mathematical Sciences who may be available to supervise your PhD project

Alex Cox

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

Andreas was instrumental in the development of SAMBa and was Co-Director from its inception until the end of 2022. He has left the University of Bath to take up the role of Chair of Probability at the University of Warwick.

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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
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Avi Mayorcas

  • Regularisation by noise in stochastic partial and ordinary differential equations
  • Stochastic quantisation of physical field theories
  • Game theory and mean field dynamics in macroeconomics and finance
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Ben Adams

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

  • Mathematical biology (growth, mechanics, and microscale locomotion)
  • Fluid mechanics (Stokes flow and asymptotic techniques)
  • Dynamical systems (reaction-diffusion problems, asymptotic analysis)
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Cécile Mailler

  • Branching processes
  • Pólya’s urns and stochastic approximation
  • Random networks
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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
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Chris Jennison

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

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

  • Percolation and lattice spin models
  • Probability on groups
  • Self-avoiding walk
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Clarice Poon

Clarice Poon has now left the University of Bath.

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Daniel Kious

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

  • 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
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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
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Federico Cornalba

  • Modelling of large-scale interacting particle systems
  • Analysis and numerics of stochastic PDEs of Fluctuating Hydrodynamics
  • Reinforcement Learning methods
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Haiyan Zheng

  • Adaptive designs in clinical trials
  • Bayesian data augmentation
  • Finite mixture distributions
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Hartmut Schwetlick

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

  • Numerical methods for stochastic differential equations (SDEs)
  • Langevin Monte Carlo and applications of SDEs to machine learning
  • Neural differential equations and kernel methods for time series data
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Jane White

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

  • Mathematical medicine
  • Fluid mechanics
  • Mathematical modelling
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Jey Sivaloganathan

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

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

  • Functional data analysis
  • Shape Statistics
  • Applications of Statistics
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Kari Heine

  • Sequential Monte Carlo and Markov Chain Monte Carlo methods
  • Martingales and Markov processes
  • Computational methods in population genetics
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Karim Anaya-Izquierdo

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

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

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

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

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

  • Model reduction
  • Control theory
  • Analysis
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Mathew Penrose

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

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

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

  • Analysis of Partial Differential Equations
  • Fluid dynamics (especially free boundary problems and nonlinear singularity formation)
  • Shock waves (their formation, structure and dynamics)
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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
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Miles Wheeler

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

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

Paul was instrumental in the development of SAMBa and was Co-Director from its inception until August 2023. He has left the University of Bath to take up the role of professor and head of the mathematics department at Penn State University.

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Philippe Trinh

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

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

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

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

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

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

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

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

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

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

Tom’s research interest is in medical statistics, with a particular focus on the design and analysis of adaptive clinical trials.

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Tim Rogers

  • Graphs and networks
  • Applied stochastic processes
  • Emergent phenomena
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Tony Shardlow

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

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

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

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

  • Inverse problems and imaging
  • Machine learning in infinite dimensions
  • Non-smooth variational problems
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Second Supervisors

Find out more about academic staff across the University who may be available to join your supervisory team

Department of Economics

Department of Electronic & Electrical Engineering

Department of Psychology

Department of Social & Policy Sciences