Professor Marc Deisenroth is the DeepMind Chair of Machine Learning and Artificial Intelligence at University College London and the Deputy Director of UCL’s AI Centre. He also holds visiting faculty positions at the University of Johannesburg and Imperial College London. Marc leads the Statistical Machine Learning Group at UCL. His research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making.

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 received Paper Awards at ICRA 2014, ICCAS 2016, ICML 2020, and AISTATS 2021. 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.

**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

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels.
*Advances in Neural Information Processing Systems (NeurIPS)*.

(2021).
(2021).
Aligning Time Series on Incomparable Spaces.
*Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)*.

(2021).
Learning Contact Dynamics using Physically Structured Neural Networks.
*Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)*.

(2021).
Matérn Gaussian Processes on Graphs.
*Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)*.

(2021).
- m.deisenroth@ucl.ac.uk
- 90 High Holborn, London, WC1V 6LJ