Statistical Science and Operational Research

Overview: Current research interests include: Bayesian networks and probabilistic graphical models Continuous and discrete optimisations Cost modelling and effectiveness analysis Decision trees, clustering, random forests and other aspects of data mining Longitudinal data analysis Non-parametric estimation Simulation, real-time modelling Survival analysis, Markov modelling and stochastic processes Research projects are focused on developing statistical and operational research techniques that can contribute to real-life applications. Further to the two main streams of research application in healthcare modelling and transportation systems, projects currently extend beyond this to application fields such as resource management, gene expression modelling, student progression modelling, GIS, risk, crime and financial market abuse. RESEARCH STRENGTHS Research strengths stem from a strong background and substantial grant income in health care research, in particular, the development of the discrete conditional phase-type models to predict patient stay and costs in hospital; the work on identification of patient characteristics interacting and impacting survival through the use of Bayesian networks and models to represent the flow of traffic in road networks. Share this course Share