Keith Harris


Position: Teaching fellow
Email:
Office: I18 Hicks building
Photo of Keith Harris

Teaching:

MAS293 Statistical Modelling (NJTech) Information  
MAS6005 Professional Skills for Statisticians Information uses MOLE
MAS6006 Statistical Consultancy Information uses MOLE


Research:


Interests: Applied Bayesian statistics
Research group: Statistics

Biography:

Keith Harris received a BSc degree in mathematics with statistics from the University of York, York, UK, in 2002, and MSc and PhD degrees in statistics from the University of Sheffield, Sheffield, UK, in 2003 and 2008, respectively. The title of his PhD thesis was "Statistical Modelling and Inference for Radio-Tracking" and he was supervised by Professor Paul Blackwell. Before leaving the University of Sheffield, he spent just over 5 months working as a PDRA for the Centre for Terrestrial Carbon Dynamics with Professor Tony O'Hagan on a project entitled "Quantifying Uncertainty in the Biospheric Carbon Flux for England and Wales". From April 2008 to August 2013, he was a PDRA at the University of Glasgow, first working on a project entitled "Classifiers in Medicine and Biology (Advancing Machine Learning Methodology for New Classes of Prediction Problems)" in the Department of Computing Science with Professor Mark Girolami, and later on a Unilever funded project in the School of Engineering with Dr. Chris Quince that focused on developing new statistical methods for the analysis of genomics data from microbial communities. From September 2013 to August 2016, he was a PDRA in the School of Mathematics and Statistics at the University of Sheffield working on an EPSRC funded project called "Simulation Tools for Automated and Robust Manufacturing" with Professor Jeremy Oakley, Dr. Eleanor Stillman and Dr. Kostas Triantafyllopoulos. Since that time, he has worked as a University Teaching Associate in statistics at SoMaS.

Research interests:

My research is in applied Bayesian statistics. Application areas that I am interested in include statistical ecology, genomics, microbiomics and advanced manufacturing.