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


Marc Deisenroth

DeepMind Chair of Machine Learning and Artificial Intelligence

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



Sophie Ostler


Research Fellows


So Takao

Research Fellow

Machine learning, Climate science, Fluid mechanics, Geometric mechanics


Yasemin Bekiroğlu

Senior Research Fellow

Machine learning, Robotics

PhD Students


Samuel Cohen

PhD Student

Machine learning, Optimal transport, Gaussian processes


Mihaela Rosca

PhD Student

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


Alexander Terenin

PhD Student

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


Sicelukwanda Zwane

PhD Student

Machine learning, Robotics, Transfer Learning, Reinforcement Learning


Steindór Sæmundsson

PhD Student

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


Janith Petangoda

PhD Student

Machine learning, Meta learning, Differential geometry, Reinforcement learning


James Wilson

PhD Student

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


Yicheng Luo

PhD Student

Robotics, Meta learning, Probabilistic programming


Jackie Kay

PhD Student

Machine learning, Robotics, Reinforcement Learning, Meta learning


Jacob Menick

PhD Student

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


Jake Cunningham

PhD Student



K. S. Sesh Kumar

Research Associate

Machine learning, Discrete optimization, Differential privacy, Submodularity


Mirgahney H. Mohamed

PhD Student

Computer vision, Uncertainty estimation


Oscar Key

PhD Student

Probabilistic modeling, Approximate inference, Machine learning, Climate science



Riccardo Moriconi

PhD (10/2016-02/2021)

Machine learning, Gaussian processes, Bayesian optimization


K. S. Sesh Kumar

Research Associate

Machine learning, Discrete optimization, Differential privacy, Submodularity


Hugh Salimbeni

PhD (10/2015-10/2019)

Machine learning, Deep probabilistic models, Approximate inference


Sanket Kamthe

PhD (10/2016-03/2021)

Machine learning, Reinforcement learning, Optimal control, Copulas


Benjamin Chamberlain

PhD (10/2014-08/2018)

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


Simon Olofsson

PhD (06/2016-03/2020)

Machine learning, Bayesian optimization, Mechanistic models, Model discrimination

Recent Blog Posts

Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics …

Discretization Drift in Two-Player Games

In this work, we quantify the discretisation error induced by gradient descent in two-player games, and use that to understand and …

Matérn Gaussian Processes on Graphs

Gaussian processes are a model class for learning unknown functions from data. They are particularly of interest in statistical …

Probabilistic Active Meta Learning (PAML)

Meta-learning can make machine learning algorithms more data-efficient, using experience from prior tasks to learn related tasks …

Learning Contact Dynamics using Physically Structured Neural Networks

Learning models of physical systems can sometimes be difficult. Vanilla neural networks—like residual networks—particularly …

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

Three papers accepted at ICML 2021

Our group got three papers accepted at ICML 2021. Very well done to everyone and congratulations to some great work!

Papers accepted at AIES 2021

Congratulations to Jackie Kay for an accepted paper at AIES. Great work!

Best Student Paper Award at AISTATS 2021

Our paper on Matern Gaussian processes on graphs has been awarded the Best Student Paper Award at AISTATS

Dr. Kamthe

Dr. Sanket Kamthe successfully passed his PhD viva

Dr. Moriconi

Dr. Riccardo Moriconi successfully passed his PhD viva

Recent & Upcoming Talks

Geoff Pleiss: Understanding Neural Networks through Gaussian Processes, and Vice Versa

Neural networks and Gaussian processes represent different learning paradigms: the former are parametric and rely on ERM-based …

Rachel Prudden: Stochastic Downscaling for Convective Regimes with Gaussian Random Fields

Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the …

Willie Neiswanger: Going Beyond Global Optima with Bayesian Algorithm Execution

In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function …

Florin Gogianu and Tudor Berariu: Spectral Normalisation in Deep Reinforcement Learning

We are happy to present this joint work with Mihaela Roșca, Răzvan Pascanu, Lucian Bușunoiu and Claudia Clopath on the effect of …

Steve James: Learning Portable Hierarchies for Task-Level Planning

In this work, we tackle the problem of learning symbolic representations of low-level and continuous environments. We present a …