Dr Marc Deisenroth

Lecturer

Address
Department of Computing
Imperial College London
180 Queen's Gate
Room 304A (Huxley Building)
London SW7 2AZ
UK
Phone
+44 (0) 20 7594 8234
Email
m.deisenroth@imperial.ac.uk
Twitter
https://twitter.com/mpd37
LinkedIn
http://www.linkedin.com/pub/marc-deisenroth/83/740/91b
Blog
https://gridworld.wordpress.com/
Public Key
http://keyserver.ubuntu.com/pks/lookup?op=get&search=0xC648F0F66D8163DF

Marc is Lecturer (equivalent to an Assistant Professor in the US) in Statistical Machine Learning in the Department of Computing at Imperial College London. From September 2013 to June 2015 he was a Research Fellow in the Department of Computing at Imperial College. From December 2011 to August 2013 he was a Senior Research Scientist & Group Leader (Learning for Control) at TU Darmstadt (Germany). From February 2010 to December 2011, he was a full-time Research Associate at the University of Washington (Seattle). Marc completed his PhD at the Karlsruhe Institute of Technology (Germany) in 2009. He conducted his PhD research at the Max Planck Institute for Biological Cybernetics (2006–2007) and at the University of Cambridge (2007–2009).

Marc’s research interests center around methodologies from modern Bayesian machine learning and their application autonomous control and robotic systems. Marc’s goal is to increase the level of autonomy in robotic and control systems by modeling and accounting for uncertainty in a principled way. Potential applications include intelligent prostheses, autonomous robots, and healthcare assistants.

Marc’s Google Scholar Citations

Key Publications

  1. Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
  2. Robust Filtering and Smoothing with Gaussian Processes, IEEE Transactions on Automatic Control, 2012
  3. Distributed Gaussian Processes, ICML, 2015
  4. Expectation Propagation in Gaussian Process Dynamical Systems, NIPS, 2012
  5. Gaussian Process Dynamic Programming, Neurocomputing, 2009
  6. A General Perspective on Gaussian Filtering and Smoothing, ACC, 2011
  7. A Survey on Policy Search for Robotics, NOW Publishers, 2013
  8. Probabilistic Movement Modeling for Intention-based Decision Making, International Journal of Robotics Research, 2013

Student Supervision

A summary can be found here.

Keynotes, Tutorials and Plenary Talks

Gaussian Process Summer School, University of Sheffield, UK, 2015 [slides], [notes], [video]
Sloan Summit, London Business School, UK, 2015
Machine Learning Summer School
, Chalmers University, Sweden, 2015 [slides]
Highlights Talk at AISTATS
, Iceland, 2014
Machine Learning Summer School on Big Data, Tunisia, 2013 [slides]

Prizes and Awards

ICML-2015 Reviewer Award
NVIDIA Academic Grant (2015)
Imperial College Junior Research Fellowship
Award (2014)
Best Paper Award (Cognitive Robotics) at ICRA 2014
NIPS-2013 Best Reviewer Award

 

Projects

CompLACS (EU Project): 12/2011 – 12/2014

Outreach and Public Understanding of Science

Imperial College Science Festival (2014)
University of Washington Industrial Affiliates (2010, 2011)

Intel Labs Open House (2010). Check out the Seattle Times video and the article in the Puget Sound Business Journal.

 

Memberships and Committees

Since 2015: Centre for Process Systems Engineers
Since 2014: Management Committee of Imperial College’s Robotics Network
Since 2014: Open Access Committee at Imperial College
Since 2013: EPSRC Network on Computational Statistics and Machine Learning
Since 2012: Steering Committee of the European Workshop on Reinforcement Learning

Conference Co-Organization

2013: Workshops Chair of the Robotics: Science & Systems Conference (RSS)
2013: Co-Organizer of the International Symposium on Adaptive Motion of Animals and Machines (AMAM)
2012: Program Chair of the European Workshop on Reinforcement Learning (EWRL)

Workshop Co-Organization

2016: Workshop on Data-Efficient Machine Learning at ICML
2016: Workshop on Locomotion and Manipulation with Complex Robotic Systems in the Real World
2016: 1st UK Manipulation Workshop
2014:  NIPS workshop on Novel Trends and Applications in Reinforcement Learning
2013:  NIPS workshop on Advances in Machine Learning for Sensorimotor Control
2009: NIPS workshop on Probabilistic Approaches for Robotics and Control

Review Editor

2016: Frontiers in Robotics and AI (OA journal)
2015: Frontiers in Robotics and AI (OA journal)
2014: Frontiers in Robotics and AI (OA journal)

Area Chair and Associate Editor

2016: NIPS
2015: ICRA, NIPS
2014: ICRA
2013: ACML

Program Committee

2015: RLDM
2014: AAAI, ACML
2013: ADPRL, IJCAI, RSS
2012: ACML, ECML/PKDD, ICPRAM, RSS
2011: RSS
2010: AAAI, RSS

Reviewing

2016: AISTATS, RSS
2015:  AISTATS, ICML, JMLR, Cambridge University Press
2014:  AISTATS, Automatica, ICML, JBHI, MLJ, NIPS, RAS
2013: ICRA, IROS, KI Magazine, MLJ, NIPS, Springer
2012: AISTATS, Humanoids, ICML, IEEE-TPAMI, NIPS, Springer
2011: CDC, ECC, ICML, ICRA, IROS, MLJ, NIPS
2010: ACC, AISTATS,  IJRR
2009: AURO,  CDC, ICML, ICRA, SCI,  IEEE-TPAMI
2008: Adaptive Behavior, IPSN, NIPS, RO-MAN
2007: IEE Electronic Letters

I also frequently review grant proposals for various funding bodies.

 

Recorded Talks

Applications of Bayesian Optimization to Systems (NIPS Workshop on Bayesian Optimization: Scalability and Flexibility, 2015)

 

Fast Robot Learning with Gaussian Processes (Workshop on Gaussian Processes for Global Optimization, 2015)

 

 

Distributed Gaussian Processes (Gaussian Process Summer School, 2015)

 

Distributed Gaussian Processes (ICML, 2015)

Bayesian Machine Learning for Controlling Autonomous Systems (LSOLDM, 2013)

PILCO: A Data-Efficient and Model-based Approach to Policy Search (ICML, 2011)

Analytic Moment-based Gaussian Process Filtering (ICML, 2009)

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