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 |  |
AS&RU |
| 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) | |
| Dr Marta Catalano |  |
Bayesian nonparametrics, statistical optimal transport and Wasserstein distances, stochastic processes and random measures |
| Ms Sherry Chen |  |
Tutor |
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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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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 dynamical systems. Periodic time series and oscillations in biological systems. Parameter estimation for (stochastic) differential equations. Molecular population dynamics. Genetic regulatory systems. |
| 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 |
| Professor Jane 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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AS&RU Monte Carlo methods, spatial point processes, multi-target tracking and uncertainty representation |
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| 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. |
| Mr Sherman Khoo |  |
Tutor |
| Dr Alisa Kirichenko |  |
Asymptotic properties of Bayesian procedures; relational data, such as networks and graphs; and performance guarantees for machine learning algorithms. High-dimensional and nonparametric methods |
| Dr Jere Koskela |  |
Monte Carlo methods, inference from diffusions and other stochastic processes, coalescent processes, mathematical population genetics |
| 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 |
| Mr Mike Liao |  |
Tutor |
| Dr Krzysztof Łatuszyński |  |
Markov chain Monte Carlo, adaptive Monte Carlo, stochastic simulations and Bayesian statistics |
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Senior Teaching Fellow | |
| Professor Chenlei Leng |  |
Statistical analysis of big and small datasets |
| Dr Gechun Liang |  |
Mathematical finance and stochastic analysis |
| Dr David Martinez |
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Dynamical Systems and their applications to physical problems |
| Professor Shahar Mendelson |  |
Statistical learning theory, empirical processes theory and asymptotic geometric analysis |
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Probability, mathematical finance, statistics and numerical stochastics | |
| Professor Giovanni Montana |  |
Data science, especially machine learning for medical imaging and reinforcement learning |
| Dr Joan Nakato |
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Senior Teaching Fellow |
| 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 |
| Mr Oliver Robinson |  |
Tutor |
| Dr Tommaso Rosati |  |
Probability and stochastic analysis: stochastic partial differential equations, particle systems and their long-time properties |
| Professor Jim Smith |
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AS&RU Environmental modelling. Game theory. Bayesian decision theory. Foundations of statistics. Business time series. Influence diagrams. Graphical methods. |
| Mr Steven Soutar |  |
Bayesian inference applied to problems within the medical and biological sciences, mathematical and statistical pedagogy, and R programming |
| Dr Dario Spanò |  |
Mathematical population genetics. Bayesian non-parametric statistics. Combinatorial stochastic processes. Measure-valued processes. |
| Dr 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 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 Yi Yu |
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High-dimensional statistics, network studies, survival analysis and applications in brain imaging data |
Research Fellows:
| Dr Austin Brown |
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Computational efficiency of Markov chain Monte Carlo algorithms |
| Dr Raiha Browning |  |
Bayesian non-parametric methods and Hawkes processes |
| Dr Alice Corbella |  |
Inference and prediction in epidemic models. Bayesian evidence synthesis, stochastic processes, state-space models, (sequential) Monte Carlo methods, multi scale models |
| 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 Jorge Gonzalez Cazares |
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Simulation algorithms and convergence of Monte Carlo methods (particularly when connected to Markov, Lévy and exchangeable increment processes) |
| Ms Ella Kaye |  |
Research Software Engineer, working on sustainability and EDI (Equality, Diversity and Inclusion) in the R Project |
| Mengchu Li |  |
Change point analysis, high-dimensional statistics, algorithmic robust statistics, minimax theory and differential privacy analysis |
| Dr Linda Nichols |
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AS&RU |
| Dr Juan Kuntz-Nussio |  |
Probability, stochastic processes, applied probability, stochastic modelling, stochastic analysis, Markov processes, Markov chains, Bayesian inference, random sampling |
| Dr Preetha Ramiah |  |
Machine Learning and Artificial Intelligence |
| Dr Lionel Riou-Durand |
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Monte-Carlo methods and their theoretical guaranties, from both asymptotic and non-asymptotic point of view |
| Dr Isao Sauzedde |  |
Planar Brownian motion, rough paths, random matrix, differential geometry and Gauge fields |
| Dr Qingying Shu | AS&RU | |
| Dr Jure Vogrinc |  |
Markov chain Monte Carlo, optimal scaling, variance reduction. Rare event simulation |
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 |
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 Murray Pollock |
Computational statistics, Cryptography, Monte Carlo methodology, Perfect simulation, Risk modelling, Stochastic differential equations |
| Dr Shahin Tavakoli |
Functional data analysis, and applications to (neuro-)imaging, genomics, phonetics, biophysics, and econometrics |
Honorary Teaching Fellows:
| Dr Panayiota Constantinou | Teaching Fellow |