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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Yicheng Luo

PhD Student

Robotics, Meta learning, Probabilistic programming

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Jackie Kay

PhD Student

Machine learning, Robotics, Reinforcement Learning, Meta learning

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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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Mihaela Rosca

PhD Student

Generative models, Reinforcement learning, Natural language processing, Scalable and safe machine learning.

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Sicelukwanda Zwane

PhD Student

Machine learning, Robotics, Transfer Learning, Reinforcement Learning

Undergraduates

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Ilana Sebag

MSc Student

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Julian Rodert

MSc Student

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Lucas Cosier

BASc Student

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Nanxi Zhang

BSc Student

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Shreshth Malik

MSc Student

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Zuka Murvanidze

BSc Student

Affiliates

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K. S. Sesh Kumar

Research Associate

Machine learning, Discrete optimization, Differential privacy, Submodularity

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Mirgahney H. Mohamed

PhD Student

Computer vision, Uncertainty estimation

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Oscar Key

PhD Student

Probabilistic modeling, Approximate inference, Machine learning, Climate science

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 News

Welcome Dr. So Takao

So Takao joins as a Research Fellow working on ‘Machine Learning for Climate Science’

Welcome, Jackie Kay

Jackie Kay joins the group as a PhD student

Welcome, Sicelukwanda Zwane

Sicelukwanda Zwane joins the group as a PhD student.

Welcome, Yicheng Luo

Yicheng Luo joins the group as a PhD student

Two Papers Acccepted at NeurIPS 2020

Two papers from our group have been accepted at NeurIPS 2020

Recent Publications

Matérn Gaussian Processes on Riemannian Manifolds

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

Probabilistic Active Meta-Learning

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

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 …

Recent & Upcoming Talks

Ti John: Gaussian processes for fun and profit: Probabilistic machine learning in industry

When companies, whether start-ups or big corporations, talk about machine learning they usually mean some kind of neural network model. …

Thu Nguyen Phuoc: Neural rendering and inverse rendering using physical inductive biases

Computer graphics focuses on rendering high-quality 2D images from 3D scenes, with most research focusing on simulating elements of the …

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, …