Marc Deisenroth

Senior Lecturer in Statistical Machine Learning

Department of Computing

Imperial College London

Marc Deisenroth is a Senior Lecturer (equivalent to an Associate Professor in the US) in Statistical Machine Learning at the Department of Computing, Imperial College London, and the PI of the Statistical Machine Learning Group. Since September 2016, Marc has also been an advisor to PROWLER.io, a Cambridge-based startup. Marc's research interests center around data-efficient and autonomous machine learning.

Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013 and received Best Paper Awards at ICRA 2014 and ICCAS 2016. In 2018, Marc has been awarded The President's Award for Outstanding Early Career Researcher. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Scholarship. Marc is a co-leader of the Machine Learning Initiative at Imperial, the AI@Imperial Network of Excellence and the Director of the Machine Learning Lab in Imperial's Data Science Institute.

Marc co-organizes the Machine Learning Summer School 2019 in London with Arthur Gretton.

Marc is currently on sabbatical at the African Institute for Mathematical Sciences (Rwanda), where he teaches a course on Foundations of Machine Learning as part of the African Masters in Machine Intelligence.

Research Expertise

  • Machine Learning: Data-efficient machine learning, Gaussian processes, reinforcement learning, Bayesian optimization, approximate inference, deep probabilistic models
  • Robotics and Control: Robot learning, legged locomotion, planning under uncertainty, imitation learning, adaptive control, robust control, learning control, optimal control
  • Signal Processing: Nonlinear state estimation, Kalman filtering, time-series modeling, dynamical systems, system identification, stochastic information processing

Key Publications

  1. Meta Reinforcement Learning with Latent Variable Gaussian Processes, Conference on Uncertainty in Artificial Intelligence, 2018
  2. Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, International Conference on Artificial Intelligence and Statistics, 2018
  3. Doubly Stochastic Variational Inference for Deep Gaussian Processes, Advances in Neural Information Processing Systems, 2017
  4. Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
  5. Robust Filtering and Smoothing with Gaussian Processes, IEEE Transactions on Automatic Control, 2012
  6. Distributed Gaussian Processes, International Conference on Machine Learning, 2015
  7. A Survey on Policy Search for Robotics, NOW Publishers, 2013