The Statistical Theory and Methodology group develops novel underpinnings for statistical practice, and implements them in new methods.

The group carries out a broad range of computational biology research including Genetics, Genomics and Epidemiology. The research is both theoretical and applied, generating both new methods and genetic and epidemiological insights as well as computational tools and software. 

The Probability Group in the Statistics Department carries out research in random graphs and networks, random trees, branching processes, Lévy processes, interacting particle systems, queueing processes, models from statistical mechanics, coalescent processes and random walks.

The Oxford Protein Informatics Group (OPIG) is an interdisciplinary group that works across the boundaries of statistics and computation and biology and medicine. We investigate both proteins and small molecules. Collaborating with academic and industrial partners, we develop cutting-edge computational methods that use the latest experimental data to yield valuable insights into immunology, in silico drug design, and protein folding.

The research of the group focuses on the development and mathematical/statistical analysis of algorithms that extract information from high-dimensional noisy data sets, network time series, and certain computationally-hard inverse problems on large graphs; causal inference, in particular the identification and estimation of causal effects in situations where standard estimation techniques are invalid due to the presence of unobserved confounders; statistical and mathematical problems that arise from the study of biological populations, devoted in particular to questions of biological ageing, including problems of mathematical evolutionary theory, mathematical ecology, methods for longitudinal social and medical data, and survival analysis.

Our research spans the whole range of modern computational statistics and machine learning with particular strengths in probabilistic modelling, nonparametric methods, Monte Carlo, variational inference, deep learning, and applications in genetics, genomics and medicine.

The analysis of biological data such as DNA sequences, gene expression arrays and single cell data can reveal new insights into intracellular mechanisms as well as evolutionary processes. This research group develops computational and statistical methods for the analysis of such data, with particular emphasis on methods for biological networks and biological sequences. 

Statistics is the ultimate transferable skill that can unlock answers to a wide range of questions. The discipline requires a core mathematical ability combined with a judicious view of problems and situations. Our rigorous teaching will give you the foundations to use statistics to solve real world problems in a wide range of fields.

Oxford University Statistical Consulting (OxUSC) is a source of statistical advice and expertise for clients from businesses and organisations and for researchers and staff at the University of Oxford. We work closely with our clients across disciplines and sectors to answer questions relating to study design, data collection, analysis and visualisation of results. We also offer statistical training tailored to your context and needs.

The Department of Statistics has an established history of working with industry across research collaborations and studentships. We welcome new industrial partnerships and are always willing to discuss new opportunities to connect.