Recent & Upcoming Talks

In order to leave the factory floors and research labs, future robots must abandon their stiff and pre-programmed movements and be …

The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their …

I will discuss multiple-data-source prediction and modelling problems arising in a number of fields, for instance in omics-based …

We derive a family of Monte Carlo estimators for gradients of expectations, which is related to the log-derivative trick, but involves …

In machine learning, new learning algorithms are designed by borrowing ideas from optimization and statistics followed by an extensive …


Principal Investigators


Marc Deisenroth

DeepMind Chair in Artificial Intelligence

Machine learning, Gaussian processes, Reinforcement learning, Robotics, Meta learning

Postdoctoral Researchers


K. S. Sesh Kumar

Research Associate

Machine learning, Discrete optimization, Differential privacy, Submodularity

PhD Students


Alexander Terenin

PhD Student

Machine learning, Bayesian statistics, Geometric learning, Meta learning


James Wilson

PhD Student

Machine learning, Bayesian optimization, Practical approximate inference


Janith Petangoda

PhD Student

Machine learning, Meta learning, Differential geometry, Reinforcement learning


Riccardo Moriconi

PhD Student

Machine learning, Bayesian optimization


Samuel Cohen

PhD Student

Machine learning, Optimal transport


Sanket Kamthe

PhD Student

Machine learning, Reinforcement learning, Optimal control, Copulas


Simon Olofsson

PhD Student

Machine learning, Bayesian optimization, Mechanistic models, Model discrimination


Steindór Sæmundsson

PhD Student

Machine learning, Meta learning, Structural priors




Benjamin Chamberlain

PhD (10/2014 - 08/2018)

Machine learning, Community detection, Representation of graphs, Hyperbolic embeddings


Hugh Salimbeni

PhD (10/2015-10/2019)

Machine learning, Deep probabilistic models, Approximate inference

Recent Publications

Quickly discover relevant content by filtering publications.

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

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. It is widely believed that MCMC methods are …

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

Recent News

We are looking for a (Senior) Research Fellow at the intersection of climate science and machine learning.

We are looking for a (Senior) Research Fellow at the intersection of robotics and machine learning.

Rasmus Larsen visits SML

Samuel Cohen joins SML