He spent the last two years as a postdoctoral research fellow in the Machine Learning Department at Carnegie Mellon University. He received his PhD from the University of Cambridge in 2014, under the supervision of professor Zoubin Ghahramani. Andrew's research is focused on building scalable and interpretable frameworks for kernel learning and deep learning. His work has been applied to long range crime forecasting, public health, and policy relevant problems, time series, image, and video extrapolation, geostatistics, gene expression, natural sound modeling, Bayesian optimization, and NMR spectroscopy. Andrew has received best dissertation (G-Research, 2014), outstanding reviewer (NIPS, 2013), and best student paper (UAI, 2011) awards.