Statistics at Manchester has had a long tradition – the chair in Mathematical Statistics is one of the oldest established chairs in the UK and the prestigious Journal of Time Series Analysis had its Editor-in-Chief from Manchester (Maurice B. Priestley) since its foundation in 1980 until the end of 2012.
The group carry out research in various areas of statistics, ranging from theoretical studies to applied research. They develop novel statistical methodology for complex data, particularly arising in biology, finance, environment and medicine. They enjoy close research links with both world-class researchers and local practitioners in these areas. They also run a statistical consultancy services for University colleagues, as well as local practitioners. Their interests span application areas such as biochemical and gene regulatory networks, biomechanics, cell migration and signalling, medical imaging, medical statistics, molecular evolution and population dynamics. These topics are pursued in collaboration with life science researchers in Manchester and beyond.
Thomas works primarily on mathematical epidemiology, an area of interdisciplinary applied mathematics that involves a large number of quantitative techniques applied to the study of patterns of disease at the population level. Epidemiology is the science of counting ill people - a simple description that hides major mathematical complexity. A tremendous number of different factors combine to determine the risk that any of us might become ill, however if we could disentangle these from each other then it might be possible to target the most important factors systematically. His work is mainly on the mathematical, statistical and computational methods that are needed to understand these phenomena. Thomas currently works on the EPSRC-funded project 'Operationalising Modern Mathematical Epidemiology'.
The Centre for Biostatistics is part of the Division of Population Health, Health Services Research and Primary Care in the School of Health Sciences, in the Faculty of Biology, Medicine and Health. Staff in the Centre collaborate with clinical and scientific staff across the University in the design, implementation, analysis and reporting of health research. Members of the Centre also engage in extensive methodological research which is primarily funded by grants from the UK Medical Research Council's Methodology Research Programme and National Institute of Health Research.
Richard is a Professor of Medical Statistics in the Centre for Biostatistics, and Deputy Director of the Manchester Academic Health Science Centre Clinical Trials Unit. His work involves the development of statistical methods for causal inference, and efficacy and mechanisms evaluation. Current applications of these methods include trials of complex interventions in mental health and trial designs and associated analysis methods in stratified medicine. Other research interests include the application of causal inference methods in pharmacoepidemiology and routinely collected datasets (eHealth). He is also a member of the MRC Health eResearch Centre, part of the Farr Institute, a co-investigator and theme lead on the MRC North West Hub for Trials Methodology Research.
The Complex Systems and Statistical Physics Group is part of the Theoretical Physics Division in the School of Physics and Astronomy at the University of Manchester. Their interests focus on the application of techniques from statistical physics and nonlinear dynamics to study complex systems. They work on a wide range of topics, in particular on problems in biology, in the social sciences and in economics. This includes the modelling of horizontal gene transfer, stochastic dynamics in biological pathways, epidemic spread, problems in game theory and evolutionary dynamics, social complexity, agent-based modelling of evacuation and the analysis of time series from financial markets.
Alan's research interests lie broadly in the study of complex systems, especially in the use of ideas and techniques from non-equilibrium statistical mechanics and the theory of stochastic processes to model and understand them. Some of his recent research characterises the effects of intrinsic stochasticity on deterministic models, especially when amplified stochastic cycles arise (with applications in ecology, epidemiology, reaction kinetics, cellular reactions, and many other areas)