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Biography
I graduated from the University of Nottingham in 2001 with a PhD in Mathematics. The title of my PhD was "Epidemics with two levels of mixing" and I have continued to work in infectious disease modelling ever since.
I spent two years at Lancaster University as a PDRA before joining UMIST/University of Manchester as a temporary lecturer in 2003. After nine years at the University of Manchester and a promotion to senior lecturer (2007), I moved back to Lancaster University as a reader in 2012. I was promoted to professor of statistics in 2016 and stayed at Lancaster until June 2020 when I left to take up my current post at the University of Nottingham.
Expertise Summary
My research interests span applied probability, statistical methodology and computational statistics.
My main research interest is mathematical modelling and statistical inference for infectious diseases. From a modelling perspective my work has focussed on the effects of incorporating population structure into stochastic models on the initial stages of an epidemic and the final size of the epidemic. For inference for epidemic data I have worked on novel MCMC (Markov chain Monte Carlo) and ABC (Approximate Bayesian computation) algorithms for efficiently estimating model parameters.
My interest in MCMC extends to work on the optimal scaling of random walk Metropolis and independence sampler algorithms. I also have research interests in integer-valued time series models which are particularly important for modelling low count time series data.
Teaching Summary
I have taught a wide range of probability, statistics and mathematics modules.
In 2020/21 I will be teaching:
MATH1001 Probability
MTHS2006 Probabilistic and Numerical Techniques for Engineers
Research Summary
My research interests span applied probability, statistical methodology and computational statistics.
My main research interest is mathematical modelling and statistical inference for infectious diseases. From a modelling perspective my work has focussed on the effects of incorporating population structure into stochastic models on the initial stages of an epidemic and the final size of the epidemic. For inference for epidemic data I have worked on novel MCMC (Markov chain Monte Carlo) and ABC (Approximate Bayesian computation) algorithms for efficiently estimating model parameters.
My interest in MCMC extends to work on the optimal scaling of random walk Metropolis and independence sampler algorithms. I also have research interests in integer-valued time series models which are particularly important for modelling low count time series data.