Carl Edward Rasmussen Department of Engineering Computational and Biological Learning Lab University of Cambridge > Engineering Department > Information Engineering > Computational and Biological Learning Lab > Machine Learning Group > Carl Edward Rasmussen Carl Edward Rasmussen Professor of Machine Learning Department of Engineering Chief Scientist and Chairman Secondmind Fellow ELLIS Society Fellow The Alan Turing Institute Fellow Darwin College I'm a professor in the Machine Learning Group and head of the Computational and Biological Learning Lab in the Division of Information Engineering at the Department of Engineering in Cambridge. Research I'm interested in the theory and practice of understanding and building systems that learn and make decisions. Humans have an exceptional ability to learn from experience, which sets them apart from current artificial intelligent (AI) systems. To understand human learning and design better AI we need principled approaches to learning and decision making based on Bayesian inference in machine learning. My interests span: probabilistic inference, reinforcement learning, approximate inference (variational and MCMC), decision making, non-parametric modeling, stochastic processes and efficient learning. Publications Gaussian Processes Gaussian processes (GPs) are a principled, practical, probabilistic approach to learning in flexible non-parametric models. GPs have found numerous applications in regression, classification, unsupervised learning and reinforcement learning. Great advances have been made recently in sparse approximations and approximate inference. My book Gaussian Processes for Machine Learning, MIT Press 2006, with Chris Williams is freely available online. I also maintain the gpml matlab/octave toolbox with Hannes Nickisch, as well as the pretty outdated Gaussian Process website. Random pointers What is the growth rate of atmospheric carbon dioxide? Are you fooled by sustainable growth? A note on UK greenhouse gas emissions. Are current UK greenhouse gas emission limits fit for purpose?. Sustainable Energy - without the hot air, facts about sustainable energy by David MacKay. What is Science?, by Richard Feynman, 1966. Teaching Probabilistic Machine Learning, 4th year module 4f13, also part of the MPhil for Machine Learning and Machine Intelligence Introduction to Probability and Statistics (part 1B paper 7) Students and Postdocs David Burt Talay Cheema Miguel Garcia-Ortegon Adrià Garriga-Alonso Alessandro Ialongo Niki Kilbertus Vidhi Lalchand Sebastian Ober Robert Pinsler Ushnish Sengupta Former: Matthias Bauer, Research Scientist at DeepMind, London Jan-Peter Calliess, Senior Research Fellow, Oxford-Man Institute of Quantitative Finance and Department of Engineering Science, Oxford Lehel Csató, Professor of Computer Science, University of Babes-Bolyai, Romania Marc Deisenroth, Professor of Artificial Intellgence, University College London David Duvenaud, Assistant Professor in Computer Science and Statistics, Univeristy of Toronto Roger Frigola, Data Science Consultant, Barcelona Dilan Görür, Machine Learning Scientist, Microsoft, San Francisco Matt Hofman, Research Scientist, DeepMind Ferenc Huszár, Machine Learning Research Lead, Twitter Cortex, London Manon Kok, Assistant Professor at Delft Center for Systems and Control, Delft University of Technology Malte Kuß, Consultant, e.on, Düsseldorf Andrew McHutchon, Data Scientist, McLaren Racing Limited, Woking Rowan McAllister, post doc, EECS, UC Berkeley Hannes Nickisch, Senior Scientist, Philips Research, Hamburg Tobias Pfingsten, Team Manager, Boston Consulting Group, Düsseldorf Joaquin Quiñonero Candela, Director of Applied Machine Learning, Facebook Paul Rubenstein, Machine Learning Research Engineer, Apple, Zürich Yunus Saatçi, Machine Learning Scientist, Uber AI Labs Ryan Turner, Machine Learing Researcher, Montreal Institute for Learning Algorithms Mark van der Wilk, University Lecturer, Department of Computing, Imperial College London Andrew Wilson, Assistant Professor, Cornell University Contact Information Department of Engineering Trumpington Street Cambridge, CB2 1PZ, UK voice +44 (0) 1223 748 513 fax +44 (0) 1223 332 662 email PGP public key My office is on the fourth floor of the Baker Building room number BE4-42. | link | link | link | © Cambridge University Engineering Dept Information provided by Carl Edward Rasmussen (cer54) Last updated: Febrary 26th 2020
