School of Engineering,
University of Glasgow,
Phone: +44 (0) 141 330 6027
My current research is focused on developing new statistical methods for the analysis of genomics data from microbial communities. Of particular interest is the development of clustering models that take account of the discrete nature of species abundance data and incorporate other sources of relevant information to provide new biological insights into the composition of these bacterial communities.
Previously, I worked on a three year EPSRC funded research project with Professor Mark Girolami also at the University of Glasgow entitled Advancing Machine Learning Methodology for New Classes of Prediction Problems. During this project, he helped develop statistical methods for defining valid proteomic biomarkers and applied a novel method of predictive response-relevant clustering to gene expression data, which helped provide insight into disease processes like leukaemia and salt-sensitive hypertension. Prior to this, he spent just over 5 months as a research associate for the Centre for Terrestrial Carbon Dynamics working with Professor Tony O'Hagan at the University of Sheffield on a project entitled Quantifying Uncertainty in the Biospheric Carbon Flux for England and Wales. Keith holds a masters degree in Statistics from the University of Sheffield and a PhD ("Statistical Modelling and Inference for Radio-Tracking") from the Department of Probability and Statistics of the same university, supervised by Professor Paul Blackwell.