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.

Website: http://theoval.cmp.uea.ac.uk/~gcc/

Follow this link for details of current PhD opportunities in Computing Sciences.   I am not currently available to supervise PhD projects.

Gavin Cawley is part of the Computational Biology Group and the Knowledge Discovery and Data Mining Group

Gavin's current research interests include a continuation of his post-graduate research on neural networks in speech synthesis, and classification of atmospheric circulation patterns (also using neural networks), in collaboration with Dr Steve Dorling.

 

Selected Publications:

Saadi, K., Talbot, N.L.C., and Cawley, G.C. Optimally regularised kernel Fisher discriminant classification. Neural Networks, Volume 20, Issue 7, Page(s) 832-841, 2007.

Cawley, G. C. and Talbot, N. L. C. Preventing over-fitting during model selection using Bayesian regularisation. Journal of Machine Learning Research, Volume 8, Page(s) 841-861, 2007.

Cawley, G. C. and Talbot, N. L. C. Gene selection in cancer classification using sparse logistic regression with Bayesian regularisation. Bioinformatics, Volume 22, Number 19, Page(s) 2348-2355, 2006.

Cawley, G. C. and Talbot, N. L. C. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recognition, Volume 36, Issue 11, Page(s) 2585-2592, 2003.

Chair of Board of Examiners (Postgraduate Teaching)