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Carl Edward Rasmussen

Carl Edward Rasmussen
Professor of Machine Learning
Department of Engineering
  Cambridge University
Chairman
Prowler.io
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Fellow
Darwin College
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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 I also maintain the gpml matlab/octave toolbox with Hannes Nickisch, as well as the pretty outdated Gaussian Process website.
 Gaussian Processes for Machine Learning cover

Random pointers

What is the growth rate of atmospheric carbon dioxide?
Are you fooled by sustainable growth?
A note on UK greenhouse gas emissions.
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

Matthias Bauer
AdriĆ  Garriga-Alonso
Alessandro Ialongo
Niki Kilbertus
Manon Kok
Robert Pinsler
Paul Rubenstein

Former:

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, Univeristy Lecturer in Statistical Machine Learning, Imperial 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
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
Yunus Saatçi, Machine Learning Scientist, Uber AI Labs
Ryan Turner, Machine Learing Researcher, Montreal Institute for Learning Algorithms
Mark van der Wilk, Machine Learning Researcher, PROWLER.io, Cambridge
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 email address
PGP public key

My office is on the fourth floor of the Baker Building room number BE451.

© Cambridge University Engineering Dept
Information provided by Carl Edward Rasmussen (cer54)
Last updated: December 18th 2017