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):
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 | ![]() |
Statistical and mathematical modelling of human systems, decision support, Bayesian networks, structured expert judgement elicitation, risk and uncertainty, survival and other health outcomes |
Dr Horatio Boedihardjo | ![]() |
Rough path theory |
Dr Thomas Berrett | ![]() |
Developing statistical theory and methodology in nonparametric settings |
Dr Julia Brettschneider | ![]() |
Statistical methodology for high-dimensional molecular data, methodology for statistical analysis of high-throughput 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 |
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Probability, statistical mechanics, stochastic analysis (especially singular Stochastic PDEs) | |
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Applied probability, in particular with a focus on applications in statistical physics, such as understanding the dynamics of glassy/amorphous systems and mass condensation |
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Dr Alice Corbella | ![]() |
Inference and prediction in epidemic models. Bayesian evidence synthesis, stochastic processes, state-space models, (sequential) Monte Carlo methods, multi scale models |
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Working at the intersection of Computer Science and Statistics with research interests in machine learning and Bayesian statistics |
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Professor Xavier Didelot | ![]() |
Analysis of epidemiological and genomic data in order to understand how bacterial pathogens evolve, spread and cause diseases |
Dr Ritabrata Dutta | ![]() |
Likelihood-free Inference, Approximate Bayesian Computations, Mechanistic Network Models, Big-data Analysis, Bayes Model Selection, Bayesian Classification, Unpaired Data-Integration for Genomics and Statistical Applications in Geology, Bioinformatics and Engineering |
Dr Richard Everitt |
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Methodology for Bayesian computation, applied to statistical genetics, neuroscience, ecology, weather and climate, spatial statistics, network analysis and signal processing |
Professor Bärbel Finkenstädt-Rand | ![]() |
Time series analysis and oscillations in biological systems. Hidden Markov models (parametric, non-parametric, 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 |
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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 |
Professor Vicky Henderson |
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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, meta-analysis and missing data. Major collaborations in cerebral palsy and epilepsy |
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Monte Carlo methods, spatial point processes, multi-target tracking and uncertainty representation | |
Professor Saul Jacka | ![]() |
Stochastic differential equations. Stochastic control. Applied stochastic processes. Optimal stopping. Applications of probability in finance and economics |
Dr 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 time-change problems. |
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 path-discontinuous stochastic processes as well as Monte-Carlo simulation |
Dr Krzysztof Łatuszyński | ![]() |
Markov chain Monte Carlo, adaptive Monte Carlo, stochastic simulations and Bayesian statistics |
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Probability, continuous time Markov processes | |
Professor Chenlei Leng | ![]() |
Statistical analysis of big and small datasets |
Dr Gechun Liang | ![]() |
Mathematical finance and stochastic analysis |
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Probability, mathematical finance, statistics and numerical stochastics | |
Dr Matteo Muccioni | ![]() |
Probabilistic models and their interplay with other area of mathematics such as combinatorics, representation theory and stochastic analysis |
Professor Giovanni Montana | ![]() |
Data science, especially machine learning for medical imaging and reinforcement learning |
Dr Joan Nakato |
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Assistant Professor (Teaching Focussed) |
Dr Sam Olesker-Taylor | ![]() |
Quantifying how long it takes a randomly-evolving 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 |
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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 long-time properties |
Dr Paul Skerritt | ![]() |
Applications of symplectic and Poisson geometry to physical systems, geometric quantization and semiclassical physics |
Professor Jim Smith |
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Environmental modelling. Game theory. Bayesian decision theory. Foundations of statistics. Business time series. Influence diagrams. Graphical methods. |
Professor Dario Spanò | ![]() |
Mathematical population genetics. Bayesian non-parametric statistics. Combinatorial stochastic processes. Measure-valued 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 |
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Approximate Bayesian computation method, likelihood-free 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 | ![]() |
Semi-supervised learning, unsupervised learning/clustering, machine learning, graphical models and optimal transport |
Dr Samuel Touchard |
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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 continuous-time methods, Bayesian inference, Quasi-stationarity, 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 |
Professor Yi Yu |
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High-dimensional statistics, statistical network analysis, change point analysis and differential privacy |
Research Staff:
Dr Raiha Browning | ![]() |
Bayesian non-parametric methods and Hawkes processes |
Dr Chris Dean | ![]() |
Probability theory |
Dr Michelle Kendall | ![]() |
Statistical genetics and pathogen dynamics |
Dr Martina Favero (Visiting) |
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Mathematical population genetics, stochastic duality, epidemic models, inference from stochastic processes |
Dr Sebastiano Grazzi |
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Monte Carlo methods based on Piecewise deterministic Markov Processes and their applications |
Dr Shenggang Hu | ![]() |
Computational statistics, Monte Carlo methods and exact simulation, currently working on differential privacy under Bayesian settings. |
Ms Ella Kaye | ![]() |
Research Software Engineer, working on sustainability and EDI (Equality, Diversity and Inclusion) in the R Project |
Dr Tom Klose | ![]() |
Stochastic Analysis, in particular (singular) stochastic partial differential equations |
Dr Dimitri Konen | ![]() |
Developing tools that capture the notion of typicality (or centrality) in datasets taking their values in Euclidean spaces as well as in various non-Euclidean spaces |
Dr Evandro Konzen | ![]() |
Bayesian inference and prediction in infectious diseases systems, function-valued processes, spatial statistics, and high-dimensional time series |
Dr Huijuan Li | ||
Mengchu Li | ![]() |
Change point analysis, high-dimensional statistics, algorithmic robust statistics, minimax theory and differential privacy analysis |
Dr Linda Nichols |
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Statistical analysis of large observational cohort studies and clinical trials and interested in the use of electronic patient records for research. |
Dr Preetha Ramiah | ![]() |
Machine Learning and Artificial Intelligence |
Dr Isao Sauzedde | ![]() |
Planar Brownian motion, rough paths, random matrix, differential geometry and Gauge fields |
Dr Minwei Sun | ||
Terence Tsui | ![]() |
Stochastic analysis and its application on theoretical biology |
Dr Geronimo Uribe Bravo | ||
Rasseeda Virgo | ![]() |
Radiomics: MRI Brain Tumours, Applied statistics and Artificial Intelligence in Health Care |
Emeritus Professors:
Professor John Copas | Statistical modelling and inference. Models for censoring and selection bias. Local likelihood. Meta-analysis. Applications, particularly in medicine and criminology. |
Professor David Firth | Statistical theory and methods, including design and computation. Generalized linear and non-linear 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 |
Honorary Professors:
Dr Tim Davis | Statistical applications in engineering |
Professor Francis
Lévi |
Personalized cancer medicine using e-Health and statistical AI |
Associate Fellows:
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 |
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 |
Dr Shahin Tavakoli |
Functional data analysis, and applications to (neuro-)imaging, genomics, phonetics, biophysics, and econometrics |
Honorary Research Fellows:
Dr Ian Hamilton | Ranking based on pairwise comparison |