Follow this link for details of current PhD opportunities in Computing Sciences. But feel free to email me to discuss projects outside these areas and alternative sources of funding.

Academic Background

My principal research interests lie in machine learning, with a particular emphasis on Bayesian and kernel learning methods. I am most interested in theoretical issues and algorithms with a direct impact in the practical application of machine learning techniques, including topics such as feature selection, model selection, performance estimation, model comparison, covariate shift, dealing with imbalanced or "non-standard" data and semi-supervised learning. Most of my applied work centres on problems arising in computational biology, in collaboration with the School of Chemistry and Pharmacy (CAP) and with the nearby John Inness Centre (JIC) and Institute for Food Research (IFR). However I also have long-standing research links with the School of Environmental Sciences (ENV) and the Climatic Research Unit (CRU), working on applications of machine learning in the environmental sciences, particularly on modelling and exploiting predictive uncertainty.

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