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Department of Engineering Computational and Biological Learning Lab |
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Carl Edward Rasmussen
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Professor of Machine Learning Department of Engineering
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Chief Scientist and Chairman Secondmind |
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Fellow ELLIS Society |
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Fellow The Alan Turing Institute |
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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. I work on machine
learning and on climate change. I don't travel
professionally by air because it destroys the habitability of earth.
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.
My first mentor was David Willshaw; I
completed my MSc with Lars Kai
Hansen and PhD with Geoff Hinton.
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as the pretty outdated Gaussian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. |
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Random pointers
Are you fooled by sustainable growth?
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
Students and Postdocs
Talay Cheema
Stratis Markou
Former:
Matthias
Bauer, Research Scientist at DeepMind, London
David Burt, postdoc at MIT
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
Miguel García
Ortegón, Machine Learning Research Scientist at Novo Nordisk
Adrià
Garriga-Alonso, researcher scientist, FAR AI
Dilan
Görür, Research Scientist, DeepMind, San Francisco
Matthew W. Hoffman, Research Scientist, DeepMind
Ferenc Huszár, Senior Lecturer in Machine Learning, Department of Computer Science and Technology, Cambridge University
Alessandro Davide Ialongo, founder and CTO at even.in
Manon Kok,
Assistant Professor at Delft Center for Systems and Control, Delft
University of Technology
Niki Kilbertus, group leader at Helmholtz AI in München
Malte
Kuß, Director Controlling & Risk Management at RWE AG, Essen
Vidhi Lalchand, MIT and
Harvard Broad Institute
Andrew McHutchon, Head of Data Science, McLaren Racing Limited, Woking
Rowan McAllister, post doc, EECS, UC Berkeley
Hannes Nickisch, Senior
Scientist, Philips Research, Hamburg
Sebastian
W. Ober Senior Machine Learning Engineer at AstraZeneca
Tobias Pfingsten, Team Manager, Boston Consulting Group, Düsseldorf
Robert
Pinsler, Senior Researcher at Microsaoft Research in Amsterdam
Joaquin
Quiñonero Candela, OpenAI
Paul Rubenstein, Machine Learning Research Engineer, Apple, Zürich
Yunus Saatçi, Machine Learning
Scientist, Uber AI Labs
Ushnish
Sengupta, Senior AI Researcher, Mediatek Research
Ryan Turner, Machine Learing Researcher, Montreal Institute for Learning Algorithms
Mark van der Wilk, University Lecturer, Department of Computing, Imperial College London
Andrew Gordon Wilson, Associate Professor, New York 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.