Academic and Research Staff
This page lists the main research interests of Department staff. For more detailed information, follow the links to individual home pages.
Academic staff (Faculty):
Professor Keith Abrams 
Bayesian statistical methods, design & Analysis of RCTs, Evidence Synthesis and Health Data Science 

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 
ASRU 

Francesca Basini  Likelihoodfree Inference, Bayesian Statistics, Modelling with Dynamical Systems, Generative modelling with neural networks, Applications in Biology and Social Sciences  
Dr Horatio Boedihardjo  Rough path theory  
Dr Thomas Berrett 
Developing statistical theory and methodology in nonparametric settings 

Dr Julia Brettschneider  Statistical methodology for highdimensional molecular data, methodology for statistical analysis of highthroughput genomic and proteomic data.  
Dr Teresa Brunsdon  Applied Statistics and the interplay with machine learning methods, analysis of text data, compositional data analysis, time series. Pedagogical approaches to teaching applied statistics and to group work  
Probability, statistical mechanics, stochastic analysis (especially singular Stochastic PDEs)  
Dr Yudong Chen  Highdimensional statistics, change point detection, robust statistics, online algorithms, machine learning  
Applied probability, in particular with a focus on applications in statistical physics, such as understanding the dynamics of glassy/amorphous systems and mass condensation 

Dr Alice Corbella  Inference and prediction in epidemic models. Bayesian evidence synthesis, stochastic processes, statespace models, (sequential) Monte Carlo methods, multi scale models  
Dr Emma Davis  Mathematical models and statistical methods to analysis the dynamics of endemic and emerging diseases  
Working at the intersection of Computer Science and Statistics with research interests in machine learning and Bayesian statistics 

Professor Xavier Didelot  Analysis of epidemiological and genomic data in order to understand how bacterial pathogens evolve, spread and cause diseases  
Dr Ritabrata Dutta  Likelihoodfree Inference, Approximate Bayesian Computations, Mechanistic Network Models, Bigdata Analysis, Bayes Model Selection, Bayesian Classification, Unpaired DataIntegration for Genomics and Statistical Applications in Geology, Bioinformatics and Engineering  
Dr Richard Everitt 
Methodology for Bayesian computation, applied to statistical genetics, neuroscience, ecology, weather and climate, spatial statistics, network analysis and signal processing 

Professor Bärbel FinkenstädtRand  Time series analysis and oscillations in biological systems. Hidden Markov models (parametric, nonparametric, Bayesian). Circadian research with application to health and medicine. Physiological and movement data from wearable sensors  
Professor Jon Forster 
Methodological and computational statistics. Inference and prediction under model uncertainty. Statistical inference for categorical data. Demographic estimation and forecasting. 

Dr Miryana Grigorova 
Stochastic analysis; stochastic control; optimal stopping; game theory; risk measures; risk management; pricing and hedging of options  
Dr Karen Habermann  Stochastic analysis with connections to geometry, analysis, and numerics, and with a particular interest in hypoelliptic diffusion processes on manifolds  
Dr Kirsty Hassall  Developing methodology to combine empirical, mechanistic and stochastic models with a focus on applications in the agrienvrionment sector under decision processes  
Professor 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  
Dr Emma Horton  Branching processes, interacting particle systems, applications to radiation transport and Monte Carlo methods  
Professor Jane L 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  
Professor Paul Jenkins 
Monte Carlo methods, inference from stochastic processes, mathematical population genetics, data science and genomics 

Professor Adam Johansen  Monte Carlo Methods, Computational statistics. Time series. Bayesian inference and decision making.  
Dr Jo Kennedy  Financial mathematics. Probability theory. Duality and timechange problems.  
Dr Brett Kolesnik 
Probability theory. Random structures, geometry, algorithms, processes, etc. Interactions with combinatorics  
Professor Ioannis Kosmidis  Methods for optimal estimation and inference of statistical models, inference and computation of statistical models from big data sets, clustering methods and applications.  
Professor Andreas Kyprianou  Theory and application of diffusive and pathdiscontinuous stochastic processes as well as MonteCarlo simulation  
Dr Krzysztof Łatuszyński  Markov chain Monte Carlo, adaptive Monte Carlo, stochastic simulations and Bayesian statistics  
Probability, continuous time Markov processes  
Professor Chenlei Leng  Statistical analysis of big and small datasets  
Dr Gechun Liang  Mathematical finance and stochastic analysis  
Dr Anastasia Mantziou  Statistical analysis of networks  
Probability, mathematical finance, statistics and numerical stochastics  
Professor Giovanni Montana  Data science, especially machine learning for medical imaging and reinforcement learning  
Dr Joan Nakato 
Assistant Professor (Teaching Focussed)  
Dr Sam OleskerTaylor 
Quantifying how long it takes a randomlyevolving system to 'mix'. The canonical example is, "How many shuffles are needed to mix a deck of cards?" 

Professor Anastasia Papavasiliou  Applied probability. Stochastic filtering and control. Theory of rough paths. Applications to signal processing. Multiscale systems.  
Dr Martyn Parker 
Learner analytics, transitions to higher education, digital education  
Professor Martyn Plummer  Biostatistics, cancer epidemiology, statistical computing and Markov Chain Monte Carlo  
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 Tommaso Rosati  Probability and stochastic analysis: stochastic partial differential equations, particle systems and their longtime properties  
Professor Joe Sakshaug  Applied statistics, survey methodology, data integration, nonresponse, missing data techniques, measurement error, alternative forms of data collection, experimental design, longitudinal studies  
Dr Paul Skerritt  Applications of symplectic and Poisson geometry to physical systems, geometric quantization and semiclassical physics  
Professor Jim Smith 
Environmental modelling. Game theory. Bayesian decision theory. Foundations of statistics. Business time series. Influence diagrams. Graphical methods.  
Professor Dario Spanò  Mathematical population genetics. Bayesian nonparametric statistics. Combinatorial stochastic processes. Measurevalued processes.  
Professor Simon Spencer 
Bayesian inference applied to epidemiology, stochastic epidemic models and statistics for analytical science 

Professor Mark Steel  Bayesian statistics and econometrics. Modelling of skewness. Spatial statistics. Model uncertainty. Semi and nonparametric Bayes.  
Dr Massimiliano Tamborrino 
Approximate Bayesian computation method, likelihoodfree methods, Statistical inference for stochastic processes, statistical inference for point processes 

Dr Nick Tawn  Computational statistics  
Dr Elke Thönnes  Computational statistics with emphasis on Markov chain Monte Carlo, in particular perfect simulation, and statistical image analysis  
Dr Matthew Thorpe  Semisupervised learning, unsupervised learning/clustering, machine learning, graphical models and optimal transport  
Dr Samuel Touchard 
Bayesian statistics 

Dr Heather Turner 
Statistical modelling and statistical programming using the open source software R 

Dr Daniel Valesin  Interacting particle systems and percolation theory  
Dr Andi Q Wang 
Monte Carlo methods, particularly continuoustime methods, Bayesian inference, Quasistationarity, Scalable methods and Adaptive methods 

Dr Jon Warren  Brownian motion. Local times. Branching processes. Dynamical systems.  
Dr Haotian Xu  Time series, Change point analysis, Robust statistics and High dimensional statistics  
Dr Wenkai Xu 
Statistical machine learning, hypothesis testing, Stein's method, network statistics, informationtheoretical approaches, comparison of experiments 

Professor Yi Yu 
Highdimensional statistics, statistical network analysis, change point analysis and differential privacy 
Research Staff:
Victoria Adedara  Statistical methodologies within the field of medical statistics, with a particular focus on epidemiological studies and clinical trials  
Applied and theoretical probability  
Dr Raiha Browning  Bayesian nonparametric methods and Hawkes processes  
Dr Adrien Corenflos  Parallel statistical computing, Monte Carlo methods, statespace models  
Dr Iyabosola Busola Oronti 
AI in Healthcare, digital and Global Health, data Analytics, medical Devices, health equity and Ethical Research into Health and Climate Change 

Dr Chris Dean  Probability theory  
Emma Exall  
Dr John Fernley  CRiSM Contact process, voter model, dynamic random graphs, random walk mixing, scalefree networks 

Dr Lukas Gräfner 
Stochastic analysis, singular SDEs/SPDEs 

Dr Michelle Kendall  Statistical genetics and pathogen dynamics  
Charlie Hepburn  Offline reinforcement learning  
Dr Shenggang Hu 
Computational statistics, Monte Carlo methods and exact simulation, currently working on differential privacy under Bayesian settings. 

Ella Kaye 
Research Software Engineer, working on sustainability and EDI (Equality, Diversity and Inclusion) in the R Project 

Dr Evandro Konzen  Bayesian inference and prediction in infectious diseases systems, functionvalued processes, spatial statistics, and highdimensional time series  
Dr Linda Nichols 
ASRU 

Dr Filippo Pagani  Markov Chain Monte Carlo, Variational Inference, Tempering, Irreversibility, Piecewise Deterministic Markov Processes, Mixture Models, Variable Selection and Interaction Discovery, Bayesian Statistics, Machine Learning, Inverse Problems  
Maria PerezLara  Alternative forms of radiotherapy using protons and neutrons, and the use of semiconductor detectors for treatment verification purposes  
Dr Isao Sauzedde  Planar Brownian motion, rough paths, random matrix, differential geometry and Gauge fields  
Kristian Romano 
ASRU 

Rasseeda Virgo 
ASRU 

Dr Fan Wang  CRiSM Highdimensional statistics, change point analysis, transfer learning, network analysis 

Dr Jiefei Wei 
Safe AI and Explainable AI, Computer Vision and Medical Imaging 

Dr Feng Xu  ASRU Use and develop mathematical and statistical methods to solve realworld problems, especially in public heath 

Dr Zhengang Zhong  Variational problems on graphs, optimal control problems, shape optimization and scientific computing 
Emeritus Professors:
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 Wilfrid Kendall  Stochastic differential equations. Computer algebra in probability and statistics. Applied probability especially in relation to spatial statistics. 
Professor Vassili Kolokoltsov 
Probability and stochastic processes, mathematical physics, differential equations and analysis; optimization and games with applications to business, biology and finance. 
Professor Tony Lawrance  Statistical analysis and nonlinear modelling of financial time series. 
Professor David Wild  Statistical bioinformatics; in particular in the application of Bayesian computation to problems in systems biology, functional genomics and proteomics. 
Honorary Professors:
Dr Tim Davis  Statistical applications in engineering 
Professor Francis Levi  Personalized cancer medicine using eHealth and statistical AI 
Associate Fellows:
Dr Joris Bierkens 
Computational challenges arising in Bayesian statistics and statistical physics 
Dr Lyudmila Grigoreva 
Statistical learning and analysis of dynamic processes, Machine learning (Recurrent Neural Networks, Deep Learning) Learning for dynamical systems and dynamical systems for learning, Time series analysis, forecasting, Financial econometrics 
Dr Jeremie Houssineau  Bayesian Statistics; Representation and quantification of uncertainty; Possibility theory; Reinforcement learning 
Professor Rachel Hilliam 
Pedgogical projects in mathematics and statistics 
Dr Jere Koskela  Monte Carlo methods, statistical inference from stochastic processes and in settings with intractable likelihood, Bayesian nonparametric statistics, coalescent processes, and mathematical population genetics 
Honorary Research Fellows:
Dr Ian Hamilton  Ranking based on pairwise comparison 