He is also Adjunct Research Scientist, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
His research is in probabilistic inference in machine learning, covering both unsupervised, supervised and reinforcement learning. He is particularly interested in design and evaluation of non-parametric methods such as Gaussian processes and Dirichlet processes.
He is co-author of Gaussian Processes for Machine Learning, MIT Press, 2006. Gaussian processes are a principled, practical, probabilistic approach to learning in kernel machines. The book describes Gaussian process approaches to regression and classification. It also discusses methods for hyperparameter tuning and model selection. Detailed algorithms are given, and demonstrations and a matlab implementation allowing very general covariance structures are available at the book web site.
email: cer54 – at – cam.ac.uk