I am a member of the Artificial Intelligence & Machine Learning research group in the Department of Computer Science. My research interests centre on Machine Learning, and extend to Data Science and Artificial Intelligence – all viewed from a probabilistic modelling and Bayesian statistical perspective wherever possible. In fact, I have a general interest in pursuing probabilistic solutions to traditionally non-probabilistically framed problems in machine learning and beyond.

Some specific topics of current focus are:

• Sparse Bayesian models (the “relevance vector machine”) and related novel learning techniques
• Probabilistic approaches to tree-based pattern recognition
• Adaptive analysis of multivariate time series
• Methods for intelligent statistical automation
• New perspectives on deep neural networks
• Model-driven data mapping and visualisation techniques

Having spent several years in industry, I also maintain an interest in some specific machine learning application areas. These include medical diagnostics, automotive modelling, payment fraud detection, sports performance analysis, and interactive entertainment (where I conceived and built the “Drivatar” AI technology for the long-running Microsoft Xbox franchise “Forza Motorsport”).

Further information may be found at my personal website.