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 |
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Bayesian statistical methods, design & Analysis of RCTs, Evidence Synthesis and Health Data Science |
| Dr Larbi Alili |
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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 Martine Barons |
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ASRU |
| Dr Francesca Basini |
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Likelihood-free Inference, Bayesian Statistics, Modelling with Dynamical Systems, Generative modelling with neural networks, Applications in Biology and Social Sciences |
| Dr Horatio Boedihardjo |
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Rough path theory |
| Professor Thomas Berrett |
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Nonparametric and high-dimensional statistics; hypothesis testing; differential privacy; missing data; semi-supervised learning; RKHS methods; permutation tests; robust statistics |
| Professor Julia Brettschneider |
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Statistical methodology for high-dimensional molecular data, methodology for statistical analysis of high-throughput genomic and proteomic data |
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Probability, statistical mechanics, stochastic analysis (especially singular Stochastic PDEs) | |
| Dr Yudong Chen |
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High-dimensional statistics; robust statistics; change point detection; online statistical and machine learning algorithms; large language models; spatiotemporal econometrics |
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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 |
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Inference and prediction in epidemic models. Bayesian evidence synthesis, stochastic processes, state-space models, (sequential) Monte Carlo methods, multi scale models |
| Dr Adrien Corenflos |
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Parallel statistical computing, Monte Carlo methods, state-space models |
| Dr Emma Davis |
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Mathematical models and statistical methods to analysis the dynamics of endemic and emerging diseases |
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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 |
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Analysis of epidemiological and genomic data in order to understand how bacterial pathogens evolve, spread and cause diseases |
| Dr Ritabrata Dutta |
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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 |
| Professor 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 |
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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 |
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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 |
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Stochastic analysis with connections to geometry, analysis, and numerics, and with a particular interest in hypoelliptic diffusion processes on manifolds |
| Dr Kirsty Hassall |
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Developing methodology to combine empirical, mechanistic and stochastic models with a focus on applications in the agri-envrionment sector under decision processes |
| 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 |
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Arbitrage theory, change of numéraire, utility maximisation, financial bubbles, transaction costs, equilibria, semimartingale calculus, strict local martingales |
| Professor David Hobson |
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Probability and financial mathematics |
| Dr Emma Horton |
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Branching processes, interacting particle systems, applications to radiation transport and Monte Carlo methods |
| Professor Jane L Hutton |
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Medical statistics, with special interests in survival analysis, meta-analysis and missing data. Major collaborations in cerebral palsy and epilepsy |
| Professor Saul Jacka |
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Stochastic differential equations. Stochastic control. Applied stochastic processes. Optimal stopping. Applications of probability in finance and economics |
| Professor Paul Jenkins |
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Monte Carlo methods, inference from stochastic processes, mathematical population genetics, data science and genomics |
| Professor Adam Johansen |
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Monte Carlo Methods, Computational statistics. Time series. Bayesian inference and decision making. |
| Dr Nazem Khan |
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Mathematical finance, risk management, decentralised finance, and network theory
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| Dr Jo Kennedy |
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Financial mathematics. Probability theory. Duality and time-change problems. |
| Dr Brett Kolesnik |
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Probability theory. Random structures, geometry, algorithms, processes, etc. Interactions with combinatorics |
| Professor Ioannis Kosmidis |
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Statistical learning and inference; asymptotic statistics; high-dimensional statistics; model selection; clustering methods; statistical computing and algorithms; statistical software; inter-disciplinary applications |
| Professor Andreas Kyprianou |
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Theory and application of diffusive and path-discontinuous stochastic processes as well as Monte-Carlo simulation |
| Professor Krzysztof Łatuszyński |
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Markov chain Monte Carlo, adaptive Monte Carlo, stochastic simulations and Bayesian statistics |
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Probability, continuous time Markov processes | |
| Professor Gechun Liang |
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Mathematical finance and stochastic analysis |
| Dr Anastasia Mantziou |
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Statistical analysis of networks |
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Probability, mathematical finance, statistics and numerical stochastics | |
| Professor Giovanni Montana |
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Data science, especially machine learning for medical imaging and reinforcement learning |
| Dr Joan Nakato |
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Assistant Professor (Teaching Focussed) |
| Professor Anastasia Papavasiliou |
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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 |
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Biostatistics, cancer epidemiology, statistical computing and Markov Chain Monte Carlo |
| Professor Christian Robert |
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Bayesian analysis, computational statistics, latent variable models and applied modelling |
| Professor Gareth Roberts |
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Stochastic processes, computational statistics, Bayesian statistics and mathematical finance |
| Dr Paul Skerritt |
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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 |
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Mathematical population genetics. Bayesian non-parametric statistics. Combinatorial stochastic processes. Measure-valued processes |
| Professor Simon Spencer |
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Bayesian inference applied to epidemiology, stochastic epidemic models and statistics for analytical science |
| Professor Mark Steel |
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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 |
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Computational statistics |
| Dr Elke Thönnes |
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Computational statistics with emphasis on Markov chain Monte Carlo, in particular perfect simulation, and statistical image analysis |
| Dr Matthew Thorpe |
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Semi-supervised learning, unsupervised learning/clustering, machine learning, graphical models and optimal transport |
| Dr Samuel Touchard |
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Bayesian statistics |
| Dr William Underwood |
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Statistical theory and methodology; nonparametric estimation; kernel techniques; random forests; dependent data; mathematics of deep learning |
| Dr Daniel Valesin |
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Interacting particle systems and percolation theory |
| Dr Andi Q Wang |
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Monte Carlo methods, particularly continuous-time methods, Bayesian inference, Quasi-stationarity, Scalable methods and Adaptive methods |
| Dr Jon Warren |
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Brownian motion. Local times. Branching processes. Dynamical systems. |
| Dr Peter Whalley |
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Stochastic numerics, computational statistics, optimisation, machine learning and statistical physics. |
| Dr Wenkai Xu |
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Statistical machine learning, hypothesis testing, Stein's method, network statistics, information-theoretical approaches, comparison of experiments |
| Professor Yi Yu |
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Differential privacy; online learning; transfer learning; distributed learning; high-dimensional statistics |
Research Staff:
| Dr Alberto Bordino | Nonparametric statistics, missing and heterogeneous data, minimax theory | |
| Dr Nicola Branchini |
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Importance sampling; Monte Carlo methods; generative models; heavy tails; rare events |
| Dr Chris Dean |
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Probability theory |
| Dr John Fernley |
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CRiSM Contact process, voter model, dynamic random graphs, random walk mixing, scale-free networks |
| Dr Maximilian Graf | Mathematical statistics and the mathematics underlying machine learning | |
| Dr Lukas Gräfner |
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Stochastic analysis, singular SDEs/SPDEs |
| Dr Ibrahim Kaddouri |
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Hidden Markov Models, random graphs, nonparametric and high-dimensional statistics and statistical learning theory. |
| Dr Shishen Lin |
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Artificial Intelligence, Optimisation, and Evolutionary Algorithm. I am interested in reinforcement learning and the combination with evolutionary computation methods |
| Dr Pedro Martin-Chavez |
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Stochastic modelling through branching processes |
| Dr Tom Morrish |
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Statistical inference for multivariate stochastic systems and non-Markovian processes, computational statistics, and applications in ecology and the life sciences |
| Dr Linda Nichols |
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ASRU |
| Dr Filippo Pagani |
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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 |
| Dr Kristian Romano |
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ASRU |
| Joe Taggart |
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Bayesian modelling, patient reported outcome measures (PROMs), and electronic health record (EHR) data |
| Rasseeda Virgo |
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ASRU |
| Dr Gengyu Xue |
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Functional data analysis, differential privacy, algorithmic fairness and change point analysis |
| Dr Zhengang Zhong |
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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 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; 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 and forestry |
| Professor Valerie Isham | Probability and statistics |
| Professor Francis Levi | Personalized cancer medicine using e-Health and statistical AI |
Associate Fellows:
| Dr Sigurd Assing | Probability theory, random processes, stochastic analysis, statistical mechanics and stochastic simulation |
| Dr Joris Bierkens | Computational challenges arising in Bayesian statistics and statistical physics |
| Dr Jeremie Houssineau | Bayesian Statistics; Representation and quantification of uncertainty; Possibility theory; Reinforcement learning |
Honorary Research Fellows:
| Dr Kevin Huang | Machine Learning |
| Dr Sam Olesker-Taylor | Discrete Markov chains |