Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and PI of the Statistical Machine Learning Group at UCL. He also holds a visiting faculty position at the University of Johannesburg. From 2014 to 2019, Marc was a faculty member in the Department of Computing, Imperial College London. Marc’s 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, and EXPO-Co-Chair at ICML 2020. He received Best Paper Awards at ICRA 2014 and ICCAS 2016. In 2019, Marc co-organized the Machine Learning Summer School in London with Arthur Gretton.

In 2018, Marc has been awarded 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

Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …

Mathematics for Machine Learning is a book that motivates people to learn mathematical concepts. The book is not intended to cover …

The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the …

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics …

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to …

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Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model’s success …

We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time …

Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …

Mathematics for Machine Learning is a book that motivates people to learn mathematical concepts. The book is not intended to cover …

Gaussian processes are nonparametric Bayesian models that have been applied to regression and classification problems. One of the …