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. In Statistical Genetics we work to identify how mutations drive variability among people in health and disease risk, to understand the history of our and other species, and to understand the forces that have shaped evolution across the tree of life, whilst in Epidemiology we work to gain  robust insights into the transmission and control of outbreaks, epidemics and pandemics of infectious diseases including COVID-19, Ebola, H1N1 influenza, MERS, rabies, dengue and Zika.

Within the University of Oxford, we have close links to the Wellcome Centre for Human Genetics, the Pandemic Sciences Institute and the Big Data Institute. Members of the group have played central roles in some of the most important international collaborative projects in human genetics such as the HapMap Project, the Wellcome Trust Case-Control Consortium, the 1000 Genomes Project, the People of the British Isles Project, the Haplotype Reference Consortium, UK Biobank and the 100,000 Genomes Project. Others have worked collaboratively with the World Health Organization.

Our research group is truly collaborative. Most epidemiology students are jointly supervised by someone based elsewhere (including other University of Oxford departments such as Biology, the Nuffield Department of Medicine and the Mathematical Institute) or other organizations (including the World Health Organization, the Zoological Society London, the UKHSA, the University of Liverpool and Liverpool School of Hygiene and Tropical Medicine). We currently have around 20 research students. 

Take a look at our research, and if you're interested, get in contact. 

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.  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.

Bioinformatics and Computational Biology do not have formal definitions that anybody has to adhere to, but would typically be understood as follows: 

Bioinformatics is today extremely diverse and can include data sources from multiple levels:  Sequence Data remains a very dominating form of data, but are crucially supplemented by structural information (small molecules, macromolecules such as protein, RNA and more), expression levels of genes, concentrations of molecules in cells and tissues, and phenotypes. Such data can be analysed by simple stochastic models or lately by machine learning techniques that can use (and needs) huge amounts of very heterogenous data. 

Computational Biology has a much wider domain and include large topics such as Computational Neuroscience, Computational Embryology, Whole Cell Modelling, Biosphere Modelling and more. Computational Biology also include a larger suite of techniques such a Dynamical Systems, Partial Differential Equations, Physical Chemistry, Dynamics on Networks and more. 

The recent twin crises of the COVID-19 pandemic and climate change has highlighted the importance of Bioinformatics and Computational Biology.  Understanding the pandemic spread has relied heavily on epidemiological models and sequence data, while structural information has been crucial in finding weak points of the SARS CoV-2 virion and its replication. Understanding the detailed consequences of climate change cannot be done without modelling of the biological components (e.g. the physiology of individuals, populations, species, and ecosystems) of the biosphere under slightly changed circumstances. 

These computational approaches to the biosciences will continue for decades and could eventually achieve very detailed simulations of biological systems. The Department is very enthusiastic about this development and determined to be a key player contributing to these ambitious endeavours. 

The Econometrics and Population Statistics group develops and deploys statistical and mathematical methods to resolve questions that arise from the quantitative study of populations, markets and interventions. These include the exploration of social, economic, financial, ecological, and population-level health phenomena.

Oxford has a large and thriving community of researchers in probability, spanning the Department of Statistics and the Mathematical Institute. Within the Department of Statistics, our research interests include interacting particle systems, random trees and branching processes, random graphs and networks, percolation, mathematical population genetics, stochastic analysis and Stein’s method.

We work closely with the Stochastic Analysis group in the Mathematical Institute.

We take both standard DPhil students and DPhil students through the EPSRC CDT in the Mathematics of Random Systems  which is run jointly between the Mathematical Institute, the Department of Statistics and the Department of Mathematics at Imperial College.

Welcome to OxUSC

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.