Statistics and its Applications Group
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'.
Korbinian is a Professor in Statistics at the University of Manchester. He is interested in statistical and machine learning methodology for biomedical data science and how novel statistical and computational approaches and algorithms can aid in the analysis of large-scale complex high-dimensional data. This involves considerable challenges due to the high-dimensionality and complex structure of the data. The biomedical applications of his methods include, e.g., biomarker discovery, clinical diagnostics and systems biology.
Jianxin is a Professor of Statistics within the School of Mathematics. His research spans a number of different topic areas such as jointly modelling mean and covariance structures in longitudinal studies, statistical diagnostics in longitudinal studies, bayesian state-space modelling approaches, generalised linear mixed models, growth curve models and medical statistics, in particular its methodology study involved in randomised controlled clinical trials and epidemiology, including trial designs, pilot studies, sample size calculation and data analysis.
Tim is a lecturer in statistics with the University, his research interest primarily include (optimal) statistical design of experiments, together with associated modelling and uncertainty quantification problems. In design of experiments, he seek methods and strategies for choosing a good allocation of the limited resources in a physical or computational experiment in order to maximize the 'information' gained. An important consideration is how to quantify the amount of information that will likely be obtained.
He alternates between Bayesian and non-Bayesian, e.g. minimax, approaches. Some particular problems of interest include design for models for grouped discrete data (GLMMs), methodology for Bayesian and pseudo-Bayesian design, computer model calibration, and random design strategies, especially as applied to model-robust design theory.