I would be happy to supervise a PhD student who is interested in computational methods for Bayesian inference. In particular, the development of new MCMC and SMC algorithms for big data and intractable likelihood problems.
My research is in the areas of computational statistics and statistical machine learning, specifically Markov chain Monte Carlo, sequential Monte Carlo, Gaussian processes and approximate Bayesian computation for intractable likelihoods. Currently, I am working on the problem of efficient Bayesian inference for big data problems via distributed computing and data sub-sampling. My research has an impact in a variety of application areas including target tracking, ecology and econometrics and I am currently collaborating extensively with a number of climate scientists on environmental data science challenges.
http://www.lancaster.ac.uk/~nemeth
Jack Baker - Stochastic gradient algorithms for scalable Markov chain Monte Carlo (2015-2018).
Kathryn Turnbull - Advancements in latent space network modelling (2016-2019).