Lecturer in Statistical Machine Learning
Department of Computing, Imperial College London
Marc Deisenroth is a Lecturer (equivalent to an Assistant 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 was Program Chair of EWRL 2012, Workshops Chair of RSS 2013 and received Best Paper Awards at ICRA 2014 and ICCAS 2016. He is a recipient of a Google Faculty Research Award and a Microsoft PhD Scholarship. Marc's research interests center around data-efficient and autonomous machine learning.
- Machine Learning: Data-efficient machine learning, Gaussian processes, reinforcement learning, Bayesian optimization, approximate inference, deep probabilistic models
- Robotics/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
- Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, International Conference on Artificial Intelligence and Statistics, 2018
- Doubly Stochastic Variational Inference for Deep Gaussian Processes, Advances in Neural Information Processing Systems, 2017
- Identification of Gaussian Process State Space Models, Advances in Neural Information Processing Systems, 2017
- Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
- Robust Filtering and Smoothing with Gaussian Processes, IEEE Transactions on Automatic Control, 2012
- Distributed Gaussian Processes, International Conference on Machine Learning, 2015
- A Survey on Policy Search for Robotics, NOW Publishers, 2013