Statistical Machine Learning Group

Research group

University College London

We are 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. Applications focus on robotics, climate science, and sustainable development.

If you are interested in joining the team, 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

Research Fellows

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Yasemin Bekiroglu

Senior Research Fellow

Machine learning, Robotics

PhD Students

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Alexander Terenin

PhD Student

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

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James Wilson

PhD Student

Machine learning, Gaussian processes, Bayesian optimization, Practical approximate inference

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Samuel Cohen

PhD Student

Machine learning, Optimal transport, Gaussian processes

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Steindór Sæmundsson

PhD Student

Machine learning, Gaussian processes, Meta learning, Structural priors, Variational inference

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Riccardo Moriconi

PhD Student

Machine learning, Gaussian processes, Bayesian optimization

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Sanket Kamthe

PhD Student

Machine learning, Reinforcement learning, Optimal control, Copulas

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Jacob Menick

PhD Student

Machine learning, Generative models, Large-scale deep learning, Variational inference, Information theory, Sparsity

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Janith Petangoda

PhD Student

Machine learning, Meta learning, Differential geometry, Reinforcement learning

Undergraduates

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Neil Leiser

Machine learning, Climate science, Traffic engineering

Affiliates

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Jackie Kay

PhD Student

Machine learning, Robotics, Reinforcement Learning, Meta learning

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K. S. Sesh Kumar

Machine learning, Discrete optimization, Differential privacy, Submodularity

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Sicelukwanda Zwane

Machine learning, Robotics

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So Takao

Research Fellow

Machine learning, Climate science, Fluid mechanics, Geometric mechanics

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Yicheng Luo

PhD Student

Alumni

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Hugh Salimbeni

PhD (10/2015-10/2019)

Machine learning, Deep probabilistic models, Approximate inference

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Benjamin Chamberlain

PhD (10/2014-08/2018)

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

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K. S. Sesh Kumar

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

Recent Publications

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 …

Healing Products of Gaussian Process Experts

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

Stochastic Differential Equations with Variational Wishart Diffusions

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

Matérn Gaussian Processes on Riemannian Manifolds

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing …

Recent & Upcoming Talks

Cynthia Matuszek: TBD

Christopher Jackson: 3D Seismic Reflection Data: Has the Geological Hubble Retained Its Focus?

In their seminal paper in 2002, Joe Cartwright and Mads Huuse referred to 3D seismic reflection data as the ‘Geological Hubble’, …

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 …

Recent Blog Posts

Efficiently sampling functions from Gaussian process posteriors

Efficient sampling from Gaussian process posteriors is relevant in practical applications. With Matheron’s rule we decouple the …

Healing Products of Gaussian Process Experts

Products of Gaussian process experts commonly suffer from poor performance when experts are weak. We propose aggregations and weighting …

Matérn Gaussian processes on Riemannian manifolds

Gaussian processes are a useful technique for modeling unknown functions. They are used in many application areas, particularly in …

Recent News

Three papers accepted at ICML 2020

Our group got three papers accepted at ICML 2020. Very well done to everyone and congratulations to some great work!

Dr. Olofsson

Congratulations to Simon Olofsson for defending his PhD!

Two papers accepted at AISTATS 2020

Steindór and Alex got their papers accepted at AISTATS 2020. Congratulations!

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.