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MRC Biostatistics Unit

Summary

Simon is a senior investigator statistician at the Medical Research Council Biostatistics Unit, University of Cambridge. He completed his PhD in epidemic modelling of infectious diseases at the University of Nottingham, then became an MRC Career Development Fellow at the MRC Biostatistics Unit. Now, as a senior investigator, he is developing a research group on neuroimaging and cognition, focused on bridging the complex and multivariate aspects of ageing with statistical methodologies. He supervises several PhD students working on the statistical challenges of neuroimaging data, as well as being co-ordinator and examiner for several MPhil courses. Outside of his academic research interests, Simon is coordinator for the MRC Biostatistics Unit's public engagement activities (in particular the annual Cambridge Science Festival); a volunteer for the Science Media Centre's 'Behind the Headlines' initiative - providing comment on science press releases; and an active STEM Ambassador - going into schools to support and promote statistics. Simon is an active member of the Royal Statistical Society, currently as a member of the Education Committee and formerly Vice-Chair of the Young Statistician Section. As well as being involved in several RSS initiatives such as: the RSS Science Journalism Programme - delivering statistical training to journalists; and as a judge for the RSS Journalism Awards ; and is currently an RSS Statistical Ambassador - supporting the Society's engagement with the media and the wider promotion of statistics.

Research Interests

  • Bayesian longitudinal modelling with missing data
  • Bayesian sample size and study design
  • Developing methododolgy for simulation studies
  • Dimension reduction in a Bayesian framework
  • Statistics applied to public health
  • Promoting and supporting proper statistical methodology in research
  • Bridging innovative statistical research and practical applications to the non-statistical community
  • Application of Bayesian methodology, in particular Markov chain Monte Carlo (MCMC)
  • Developing the theory of Approximate/Exact Bayesian Computation (ABC/EBC) and its application to stochastic models
  • Stochastic epidemic modelling and inference, particularly on partially observed epidemics
  • Neuro-imaging and image analysis in a Bayesian framework
  • High performance statistical computing, parallel and distributed applications (OpenMP/MPI/GPU)

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