Statistical Machine Learning Group

University College London

The Statistical Machine Learning group is a research group at UCL’s Centre for Artificial Intelligence. Our research expertise is in data-efficient machine learning, probabilistic modeling, and autonomous decision making.

If you are interested in joining the Statistical Machine Learning group, please check out our openings.

Meet the Team

Principal Investigators

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Marc Deisenroth

DeepMind Chair in Artificial Intelligence

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

PhD Students

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James Wilson

PhD Student

Machine learning, Bayesian optimization, Practical approximate inference

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Alexander Terenin

PhD Student

Machine learning, Bayesian theory, Differential-geometric learning, Structural priors

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Riccardo Moriconi

PhD Student

Machine learning, Bayesian optimization

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Sanket Kamthe

PhD Student

Machine learning, Reinforcement learning, Optimal control, Copulas

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Janith Petangoda

PhD Student

Machine learning, Meta learning, Differential geometry, Reinforcement learning

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Samuel Cohen

PhD Student

Machine learning, Optimal transport

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Steindór Sæmundsson

PhD Student

Machine learning, Meta learning, Structural priors

Alumni

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Benjamin Chamberlain

PhD (10/2014-08/2018)

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

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Hugh Salimbeni

PhD (10/2015-10/2019)

Machine learning, Deep probabilistic models, Approximate inference

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K. S. Sesh Kumar

Research Associate (12/2017-09/2019)

Machine learning, Discrete optimization, Differential privacy, Submodularity

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Simon Olofsson

PhD (06/2016-03/2020)

Machine learning, Bayesian optimization, Mechanistic models, Model discrimination

Affiliates

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K. S. Sesh Kumar

Research Associate (12/2017-09/2019)

Machine learning, Discrete optimization, Differential privacy, Submodularity

Recent Publications

Variational Integrator Networks for Physically Structured Embeddings

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

Asynchronous Gibbs Sampling

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

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

Efficiently Sampling Functions from Gaussian Process Posteriors

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

Recent & Upcoming Talks

Yasemin Bekiroglu: Learning and multi-modal sensing for robotic grasping and manipulation

Robots are envisioned as capable machines who easily navigate and interact in a world built for humans. However, looking around us we …

Joanna Slawinska: Data-driven spectral analysis and nonparameteric predictions of climate dynamics

The Earth’s system dynamics has an intrinsically multi-scale and nonlinear nature, which fundamentally affects the ability to …

Laura Mansfield: Can we use machine learning to predict global patterns of climate change?

To achieve long-term climate change goals, such as limiting global warming to 1.5 or 2°C, there must be a global effort to decide and …

So Takao: Stochastic Advection by Lie Transport: A geometric framework for data-driven turbulence closure

Way too often, observations from weather stations fall outside of the ensemble generated by initial uncertainty in weather models. This …

Yasemin Bekiroglu: Towards Robust and Goal-oriented Robotic Grasping and Manipulation

Robots are envisioned as capable machines who easily navigate and interact in a world built for humans. However, looking around us we …

Recent News

Dr. Olofsson

Research Fellow/Senior Research Fellow position in Machine Learning for Climate Science

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

Research Fellow/Senior Research Fellow position in Machine Learning for Robotics

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

Welcome to the team, Rasmus

Rasmus Larsen visits SML