I am interested in developing flexible, interpretable, and scalable machine learning models, particularly for kernel learning and deep learning. I have expertise in probabilistic modelling, Gaussian processes, Bayesian nonparametrics, kernel methods, neural networks, scalable algorithms, and automatic machine learning. My work has been applied to time series, image, and video extrapolation, geostatistics, gene expression, natural sound modelling, kernel discovery, Bayesian optimisation, econometrics, cognitive science, NMR spectroscopy, PET imaging, and general relativity.