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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So Takao

Research Fellow

Machine learning, Climate science, Fluid mechanics, Geometric mechanics

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Yasemin Bekiroğlu

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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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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Janith Petangoda

PhD Student

Machine learning, Meta learning, Differential geometry, Reinforcement learning

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Samuel Cohen

PhD Student

Machine learning, Optimal transport, Gaussian processes

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James Wilson

PhD Student

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

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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

Undergraduates

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Carlos Xu

MSc Student

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Lorenzo Minto

MSc Student

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Neil Leiser

MSc Student

Machine learning, Climate science, Traffic engineering

Affiliates

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K. S. Sesh Kumar

Research Associate

Machine learning, Discrete optimization, Differential privacy, Submodularity

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Yicheng Luo

PhD Student

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Jackie Kay

PhD Student

Machine learning, Robotics, Reinforcement Learning, Meta learning

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Sicelukwanda Zwane

PhD Student

Machine learning, Robotics

Alumni

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K. S. Sesh Kumar

Research Associate

Machine learning, Discrete optimization, Differential privacy, Submodularity

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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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Simon Olofsson

PhD (06/2016-03/2020)

Machine learning, Bayesian optimization, Mechanistic models, Model discrimination

Recent Blog Posts

Variational Integrator Networks

Learning models of physical systems can be tricky, but exploiting inductive biases about the nature of the system can speed up learning …

High-dimensional Bayesian optimization using low-dimensional feature spaces

Bayesian optimization is a powerful technique for the optimization of expensive black-box functions, but typically limited to …

Aligning Time Series on Incomparable Space

Data is often gathered sequentially in the form of a time series, which consists of sequences of data points observed at successive …

Estimating Barycenters of Measures in High Dimensions

Barycenters summarize populations of measures, but computing them does not scale to high dimensions with existing methods. We propose a …

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 Publications

Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models

Nonparametric extensions of topic models such as Latent Dirichlet Allocation, including Hierarchical Dirichlet Process (HDP), are often …

High-Dimensional Bayesian Optimization with Manifold Gaussian Processes

Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven …

A Foliated View of Transfer Learning

Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to …

Probabilistic Active Meta-Learning

Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics …

Recent & Upcoming Talks

Chidubem Iddianozie: On the Prospects and Challenges of Machine Learning for Street Networks

Our streets shape us in more ways than we can imagine. In this talk, I offer a unique view into the promising applications of machine …

Tim G. J. Rudner: Inter-domain Deep Gaussian Processes

Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP …

Cynthia Matuszek: Robots, Language, and Environments: Modeling Linguistic Human-Robot Interactions

As robots move from labs and factories into human-centric spaces, it becomes progressively harder to predetermine the environments, …

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 …

Recent News

Paper accepted at Machine Learning Journal

Paper on high-dimensional Bayesian optimization using low-dimensional feature spaces accepted at Machine Learning Journal.

Honorable Mention Award for Outstanding Paper at ICML 2020

Our paper ‘Efficiently Sampling Functions from Gaussian Process Posteriors’ has received an Honorable Mention Award for …

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!