People » Academic and research
This page lists the main research interests of Department staff. For more detailed information, follow the links to individual home pages.
Academic staff (Faculty):
Dr Larbi Alili  Probability theory and its applications. Fluctuation theory in discrete and continuous time. Exit problems for Markov processes. Fine properties of diffusions and Lévy processes.  
Dr Sigurd Assing  Probability theory, random processes, stochastic analysis, statistical mechanics and stochastic simulation.  
Dr Martine Barons (AS&RU) 
Statistical and mathematical modelling of human systems, decision support, Bayesian networks, structured expert judgement elicitation, risk and uncertainty, survival and other health outcomes 

Dr Julia Brettschneider  Statistical methodology for highdimensional molecular data, methodology for statistical analysis of highthroughput genomic and proteomic data.  
Probability Theory, Potential Theory, Graph Theory, Ergodic Theory and Dynamical Systems.  
Senior Teaching Fellow  
Senior Teaching Fellow  
Dr David Croydon  Probability theory. Random walks on random graphs. Random fractals.  
Working at the intersection of Computer Science and Statistics with research interests in machine learning and Bayesian statistics. 

Professor Bärbel Finkenstädt Rand  Time series analysis and dynamical systems. Periodic time series and oscillations in biological systems. Parameter estimation for (stochastic) differential equations. Molecular population dynamics. Genetic regulatory systems.  
Professor David Firth  Statistical theory and methods, including design and computation. Generalized linear and nonlinear models. Applications, especially in the social and health sciences.  
Professor Simon French 
Decision Analysis; Decision Support; Risk Analysis and Communication; Statistical Techniques; Information Systems; Collaboration and the Web; Public Sector; Environmental Management; Emergency Management  
Professor Mark Girolami  Computational Statistics, Multivariate and High Dimensional Data, Biostatistics  
Dr Vicky Henderson 
Optimal stopping and optimal control problems, with applications to real options, executive stock options, and recently, behavioural finance  
Dr Martin Herdegen  Arbitrage theory, change of numéraire, utility maximisation, financial bubbles, transaction costs, equilibria, semimartingale calculus, strict local martingales  
Professor David Hobson  Probability and financial mathematics.  
Professor Jane Hutton  Medical statistics, with special interests in survival analysis, metaanalysis and missing data. Major collaborations in cerebral palsy and epilepsy.  
Professor Saul Jacka  Stochastic differential equations. Stochastic control. Applied stochastic processes. Optimal stopping. Applications of probability in finance and economics.  
Dr Paul Jenkins  Population genetics, using methods such as coalescent theory, diffusion processes, Monte Carlo sampling, and perturbation techniques.  
Dr Adam Johansen  Monte Carlo Methods, Computational statistics. Time series. Bayesian inference and decision making.  
Professor Wilfrid Kendall  Stochastic differential equations. Computer algebra in probability and statistics. Applied probability especially in relation to spatial statistics.  
Dr Jo Kennedy  Financial mathematics. Probability theory. Duality and timechange problems.  
Professor Vassili Kolokoltsov  Probability and stochastic processes, mathematical physics, differential equations and analysis; optimization and games with applications to business, biology and finance.  
Dr Krzysztof Łatuszyński  Markov chain Monte Carlo, adaptive Monte Carlo, stochastic simulations and Bayesian statistics  
Senior Teaching Fellow  
Dr Anthony Lee 
Monte Carlo Methodology, Computational Statistics, Bayesian Inference  
Professor Chenlei Leng  Statistical analysis of big and small datasets  
Statistical methods for the analysis of brain image data  
Dr Anastasia Papavasiliou  Applied probability. Stochastic filtering and control. Theory of rough paths. Applications to signal processing. Multiscale systems.  
Professor Christian Robert  Bayesian analysis, computational statistics, latent variable models and applied modelling.  
Professor Gareth Roberts  Stochastic processes, computational statistics, Bayesian statistics and mathematical finance  
Dr Rich Savage  
Statistical machine learning and its application to important problems in medicine 
Dr Ewart Shaw  Principal Teaching Fellow  
Professor Jim Smith (AS&RU) 
Environmental modelling. Game theory. Bayesian decision theory. Foundations of statistics. Business time series. Influence diagrams. Graphical methods.  
Dr Dario Spanò  Mathematical population genetics. Bayesian nonparametric statistics. Combinatorial stochastic processes. Measurevalued processes.  
Dr Simon Spencer  Bayesian inference, stochastic processes and applied probability, MCMC methods  
Professor Mark Steel  Bayesian statistics and econometrics. Modelling of skewness. Spatial statistics. Model uncertainty. Semi and nonparametric Bayes.  
Dr Elke Thönnes  Principal Teaching Fellow  
Dr Sebastian Vollmer 
Monte Carlo Methods, Stochastic Gradient Methods, Stochastic Processes 

Dr Sara Wade  Regression; density estimation; conditional density estimation; mixture models; clustering; feature allocation; (dependent) random measures; Markov Chain Monte Carlo methods; and Bayesian nonparametrics and machine learning.  
Professor David Wild  Statistical bioinformatics; in particular in the application of Bayesian statistical machine learning techniques to problems in systems biology, functional genomics and proteomics  
Dr Jon Warren  Brownian motion. Local times. Branching processes. Dynamical systems.  
Dr Nikolaos Zygouras  Probability, mathematical physics (random media, stochastic PDE's, statistical mechanics). 
Research Fellows:
Dr E HernandezHernandez 
Stochastic control in continuous time, and fractional differential equations and their connection with probability theory 

Dr Qi Huang  Time series analysis, spectrum analysis and statistical modelling  
Dr Hanne Kekkonen  Stochastic inverse problems and hyperparameters  
Dr Matt Kusner  Stateoftheart machine learning models, and models that are often used to solve realworld problems  
Dr Matt Moores  Approximate Bayesian Computation, Bayesian Inference, Computational Statistics.  
Dr Linda Nichols (AS&RU) 
Statistical analysis of large observational cohort studies and clinical trials and interested in the use of electronic patient records for research. 

Dr Murray Pollock 
Monte Carlo methods; Computational methods for SDEs; Inference for intractible models. 

Dr Panayiota Touloupou  Bayesian inference and model selection for partially observed stochastic epidemics  
Dr Seppo Virtanen  Bayesian modeling of multiple data sources 
Emeritus Professors:
Professor John Copas  Statistical modelling and inference. Models for censoring and selection bias. Local likelihood. Metaanalysis. Applications, particularly in medicine and criminology. 
Professor Tony Lawrance  Statistical analysis and nonlinear modelling of financial time series. 
Honorary Professor:
Dr Tim Davis  Honorary Professor 
Professor John Fox  Honorary Professor 
Associate Fellows:
Dr Jon Arthur 
Designs risk, resiliance, crisis prevention and issues management capabilities in multinational contexts. 
Dr Dalia Chakrabarty  Solving puzzles within Astrophysics, and using numerical and statistical algorithms. 
Dr John Fenlon  Experimental design; the analysis of discrete data, particularly related to various forms of biological assay; statistical methods in chemistry; general statistical consultancy 
Dr Anjali Mazumder  Methods in and applications of probabilistic graphical models, decision analysis, information theory, Bayesian methods, and causal inference. 
Dr Fabio Rigat  Markov chain Monte Carlo methods for Bayesian inference, utilitybased model selection, nonparametric models for survival data, network models for dynamic biological systems, nonparametric dynamic regression models, Bayesian CART models, pathway modelling 
Dr Heather Turner  Statistical modelling and statistical programming using the open source software R 