Louis Aslett

Louis’ research spans methodology and applications in computational statistics, machine learning, and reliability theory. Woven through these areas he has a particular interest in both privacy aspects and high-performance computing.

Camila Caiado

Frank Coolen

Frank works mainly on theory, methods and applications of nonparametric predictive inference, statistical reproducibility, and system reliability.

Tahani Coolen-Maturi

Tahani works mainly on the development and application of nonparametric predictive inference, with further interests in reliability, survival analysis, diagnostic accuracy, and modelling dependence.

Peter Craig

Peter works on statistical methodology and applications to risk assessment, frequently for the European Food Safety Authority. Subjectivist Bayesian foundations and random effects models support synthesis of limited expert judgements with data.

Richard Crossman

Jonathan Cumming

My research focuses on applied and computational statistics, developing Bayesian and Bayes linear methods for uncertainty analysis, emulation, and model assessment, with applications in engineering, environment, and complex simulations.

Reza Drikvandi

Reza works on high-dimensional statistics, change point analysis, statistical inference, longitudinal and survival analysis, addressing complex problems with applications in medical research, finance, and the social sciences.

Hailiang Du

Hailiang works on statistical and machine-learning methods for uncertainty quantification, probabilistic forecasting, and nonlinear dynamical systems, with applications in weather and climate modelling, energy, and environmental resilience.

Eimear Dunne

Eimear is on the Education track with an emphasis on teaching mathematics to students with a non-mathematical background and accessible module materials.

Jochen Einbeck

Jochen works on statistical methodology for a range of statistical modelling scenarios including count data models, mixture models, and nonparametric regression. On the applied side, his main interests are statistical dosimetry as well as the analysi

Michael Goldstein

Michael works on foundations, methods and applications of statistics and decision making, with particular emphasis on Bayes linear inference and Uncertainty Quantification for large computer models.

Andrew Golightly

Andrew works on Bayesian inference methodology for complex stochastic processes, including stochastic differential equations and jump processes, with applications in epidemiology, population ecology and systems biology.

John Paul Gosling

JP uses Bayesian inference and expert elicitation to bridge qualitative insights and quantitative analysis in decision-making under uncertainty. He has worked on applications in fields such as toxicological risk assessment and flood risk management.

Sarah Heaps

Sarah works primarily on Bayesian time-series analysis, latent variable modelling and the development of structured prior distributions. Her research is largely motivated by applications in the life sciences.

Andrew Iskauskas

Andrew works in uncertainty quantification and calibration for complex computer models, using Bayes Linear emulation and history matching, with applications in epidemiology and physics.

Samuel Jackson

Sam works on the Uncertainty Quantification, statistical analysis and Bayesian emulation of computer models, with applications including defence-threat reduction, epidemiology, physiology, systems biology and X-ray imaging.

Ian Jermyn

My research focuses on applied and computational statistics, developing and applying Bayesian and Bayes linear methods for uncertainty analysis, emulation, and model assessment, with applications in engineering and environmental modelling.

Sungkyung Kang

Sungkyung’s research interests include survey methodology and multilevel model analysis using population-based data. She is also interested in interpreting quantitative and qualitative findings through social and cultural lenses.

Georgios Karagiannis

Georgios is a Bayesian statistician focused on statistical modelling, uncertainty quantification, spatial statistics, computational methods for complex models, and statistical machine learning.

James Liley

James works mostly in theory and applications of predictive models, with a particular interest in making use of predictions in medical settings. He also has interests in high-dimensional statistics, data security, and clinical trials.

Hyeyoung Maeng

Hyeyoung works on change-point detection, dimension reduction, high-dimensional statistics, factor analysis and time series, with applications in finance and environment.

Cuong Nguyen

Cuong’s research includes methodology, theory, and applications of machine learning and artificial intelligence, with particular interests in the intersections of deep learning and statistics.

Emmanuel Ogundimu

Emmanuel's research focuses on variable selection, machine learning, and sample selection models, developing high-dimensional and semiparametric methods for rare-event data in biostatistics, clinical trials, credit scoring, and omics.

Rachel Oughton

Rachel’s main area of research is causal inference in controlled and observational studies, particularly in the social sciences. She is also interested in quantification of uncertainty in computer models, and is involved in several outreach projects.

Mohammad Sayari

Ian Vernon

Ian develops and applies Bayesian approaches to Uncertainty Quantification for the analysis of complex physical systems. Application areas include cosmology, epidemiology, nuclear physics, geology and systems biology.

Darren Wilkinson

Darren uses computational Bayesian inference to study stochastic dynamical systems and other complex models. He works on substantive applications in fields such as systems biology and engineering.