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Carl Edward Rasmussen

Professor of Machine Learning
Department of Engineering
University of Cambridge

Cambridge University
Adjunct Research Scientist
Max Planck Institute for Biological Cybernetics
Tübingen, Germany

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I'm a professor in the Machine Learning Group of the Computational and Biological Learning Lab in the Division of Information Engineering at the Department of Engineering in Cambridge.


I have very broad interests in probabilistic inference in machine learning, covering both unsupervised, supervised and reinforcement learning. I'm particularly interested in design and evaluation of non-parametric methods such as Gaussian processes and Dirichlet processes. Exact inference in these models is often intractable, so one needs to resort to approximation methods, such as variational techniques or Markov chain Monte Carlo.

Gaussian Processes

I have co-authored a book with Chris Williams, entitled Gaussian Processes for Machine Learning, MIT Press, 2006, online version. 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. I gave a tutorial lecture on Gaussian processes at the NIPS 2006 conference. I also maintain a web site on Gaussian processes. Gaussian Processes for Machine Learning cover

Reinforcement Learning

I'm interested in how to speed up reinforcement learning by using a model-based approach with probabilistic models. See a short demo.

Students and Postdocs

Matthias Bauer
Alessandro Ialongo
Manon Kok
Paul Rubenstein


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,, Cambridge
Andrew Wilson, Assistant Professor, Cornell University


1BP7 Part 1B Paper 7, Introduction to Probability and Statistics
4f13 Probabilistic Machine Learning


Tutorial on Gaussian Processes at NIPS 2006, slides, pdf.


At the moment, my publications can be found here (or see the Department Publications Database, which is currently under construction).

Some worthwhile things on the Web

What is the growth rate of atmospheric carbon dioxide?
A note on UK greenhouse gas emissions.
What's your view on sustainable growth?
The book Sustainable Energy - without the hot air, facts about sustainable energy by David MacKay.
What is Science?, by Richard Feynman, 1966.
Inconsistent Maximum Likelihood Estimation: An "Ordinary" Example a simple illustrative example from Radford Neal's blog.

Contact Information

Department of Engineering
Trumpington Street
Cambridge, CB2 1PZ, UK
voice +44 (0) 1223 748 513 NOTE: new phone number
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: June 2nd 2017