Marc Deisenroth

Marc Deisenroth

Google DeepMind Chair of Machine Learning and Artificial Intelligence
Director of Science & Innovation—Grand Challenges (Environment & Sustainability)

University College London

The Alan Turing Institute

Professor Marc Deisenroth is the Google DeepMind Chair of Machine Learning and Artificial Intelligence at University College London, part of the UNESCO Chair on Artificial Intelligence at UCL, and Director of Science & Innovation—Grand Challenges (Environment & Sustainability) at The Alan Turing Institute. He also holds a visiting faculty position at the University of Johannesburg. Marc co-leads the Sustainability and Machine Learning Group at UCL. His research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making with applications in climate/weather science, nuclear fusion, and robotics.

Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO Chair at ICML 2020, Tutorials Chair at NeurIPS 2021, and Program Chair at ICLR 2022. He is an elected member of the ICML Board. He received Paper Awards at ICRA 2014, ICCAS 2016, ICML 2020, AISTATS 2021, and FAccT 2023. In 2019, Marc co-organized the Machine Learning Summer School in London.

In 2018, Marc received The President’s Award for Outstanding Early Career Researcher at Imperial College. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Grant.

In 2018, Marc spent four months at the African Institute for Mathematical Sciences (Rwanda), where he taught a course on Foundations of Machine Learning as part of the African Masters in Machine Intelligence. He is co-author of the book Mathematics for Machine Learning, published by Cambridge University Press.

Research Expertise

Machine Learning: Data-efficient machine learning, Gaussian processes, reinforcement learning, Bayesian optimization, approximate inference, deep probabilistic models, geo-spatial 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