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 |