Turner Group

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I’m Rich Turner, a Professor of Machine Learning in the Machine Learning Group at the University of Cambridge and a Research Lead, AI for Weather Prediction, at the Alan Turing Institute.

I am the Cambridge Lead for the EPSRC Probabilistic AI Hub.

My previous roles include Visiting Researcher at Microsoft Research in the AI4Science and AI teams, Co-Director of the UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER CDT), and Course Director of the Machine Learning and Machine Intelligence MPhil programme. I have received over £30 million of funding as a Principal or Co-Investigator. I’ve been awarded the Cambridge Students’ Union Teaching Award for Lecturing.

My current research interests include:

  • Probabilistic Machine Learning Fundamentals (including generative models and uncertainty-aware approaches)
  • Environmental Prediction (especially for weather, earth systems, and climate prediction)
  • Spatio-temporal Modelling (especially using a fusion of large-scale deep learning and probabilistic modelling for scientific applications)

Below you’ll find information about my group.

You can stay up to date with my most recent research on Google Scholar or by following me on LinkedIn or the Cambridge MLG twitter account.

Selected work

Papers

SportsNGEN: Sustained Generation of Realistic Multi-player Sports Gameplay
Thorpe et al.

Preprint
2024

Aurora: A foundation model of the atmosphere
Bodnar et al.

Preprint
2024

Aardvark Weather: end-to-end data-driven weather forecasting
Vaughan et al.

Preprint
2024

Tutorials

Denoising Diffusion Probabilistic Models in Six Simple Steps
Turner et al.

Preprint
2024

An Introduction to Transformers
Turner

Preprint
2023

Gaussian Processes
Turner

talk
2017

Blog posts

What keeps Bayesian's up at night? Part 1: Day Time
Bruinsma, Foong, and Turner

Blog post
2021

What keeps Bayesian's up at night? Part 2: Night Time
Bruinsma, Foong, and Turner

Blog post
2021

Recent news

Naked Scientist Podcast

The quiet AI revolution taking weather forecasting by storm

Interview
Jul 2024

Qualcomm

Runa Eschenhagen wins Qualcomm Innovation Fellowship Europe

Award
Jun 2024

Cambridge Philosophical Society

The Quiet AI Revolution in Weather Forecasting

Talk
Dec 2023

Apply

If you are interested in applying to my group in Cambridge, please check the guide on the CBL Machine Learning Group website. You can also reach out to me or one of my students.

If you are interested in collaborating, please get in touch at ret26 at cam.ac.uk.

Group members

Current

Postdoc

John Bronskill

digital imaging, computer vision, signal processing

Postdoc

Ares Panos

bayesian inference, continual learning, large vision-language models

PhD

Jonathan So

probabilistic machine learning, computational neuroscience

PhD

Anna Vaughan

weather, earth systems, climate science

PhD

Isabel Chien

machine learning, healthcare, fairness

PhD

Kenza Tazi

Bayesian inference, Gaussian processes, climate science

PhD

Marc Girona-Mata

probabilistic modelling, environmental and climate sciences

PhD

Matt Ashman

probabilistic machine learning, decision making under uncertainty

PhD

Stratis Markou

probabilistic modelling, weather prediction

PhD

Aliaksandra Shysheya

diffusion models

PhD

Isaac Reid

machine learning, statistical physics, applied mathematics

PhD

Andrew McDonald

diffusion models, climate science

PhD

Bruno Mlodozeniec

probabilistic modelling, interpretability, causality

PhD

Cristiana Diaconu

neural processes, diffusion processes, uncertainty quantification

PhD

Runa Eschenhagen

training algorithms for deep learning, approximate Bayesian inference

Research assistant

Rupert Menneer

computer vision, natural language processing, diffusion processes

Former

Marcin Tomczak

PhD student | 2019-2024

Thesis: Improved Sampling and Variational Inference Methods for Neural Networks

Next: Quantitative researcher at G-Research

Emile Mathieu

Postdoctoral researcher | 2022-2024

Next: Machine learning researcher at Xaira Therapeutics

Vincent Fortuin

Postdoctoral fellow | 2022-2023

Next: Principal investigator at Helmholtz AI

Massimiliano Patacchiola

Postdoctoral researcher | 2021-2023

Next: Research engineer at Tools for Humanity

Lachlan Thorpe

Visiting industrial fellow | 2022-2023

Next: Senior machine learning software engineer at Hawk-Eye Innovations Ltd

Elre Oldewage

PhD student | 2018-2023

Thesis: Advances in Meta-Learning, Robustness, and Second-Order Optimisation in Deep Learning

Next: Application engineer at MathWorks

Will Tebbutt

PhD student | 2017-2022

Thesis: Advances in Software and Spatio-Temporal Modelling with Gaussian Processes

Next: Postdoctoral researcher at the Alan Turing Institute and University of Cambridge

Wessel Bruinsma

PhD student | 2018-2022

Thesis: Convolutional Conditional Neural Processes

Next: Senior researcher at Microsoft Research

Siddarth Swaroop

PhD student | 2017-2022

Thesis: Probabilistic Continual Learning using Neural Networks

Next: Postdoctoral fellow at Harvard University

James Requiema

PhD student | 2016-2022

Thesis: The Neural Processes Family: Translation Equivariance and Output Dependencies

Next: Postdoctoral fellow at Vector Institute and University of Toronto

Andrew Foong

PhD student | 2018-2022

Thesis: Approximate Inference in Bayesian Neural Networks and Translation Equivariant Neural Processes

Next: Senior researcher at Microsoft Research

Shixiang Gu

PhD student | 2014-2019

Thesis: Sample-Efficient Deep Reinforcement Learning for Continuous Control

Next: Research scientist at Google Brain

Michael Hutchinson

Research assistant | 2019

Next: PhD student at the University of Oxford

Yingzhen Li

PhD student | 2013-2018

Thesis: Approximate Inference: New Visions

Next: Researcher at Microsoft Research

Matejo Rojas-Carulla

PhD student | 2014-2018

Thesis: Learning Transferable Representations

Next: Research scientist at Facebook AI Research

Mark Rowland

PhD student | 2014-2018

Thesis: Structure in Machine Learning: Graphical Models and Monte Carlo Methods

Next: Research scientist at DeepMind

Cuong Nguyen

Postdoctoral researcher | 2016-2018

Next: Assistant professor in Statistics in the Department of Mathematical Sciences, Durham University

Arno Solin

Postdoctoral fellow | 2017-2018

Next: Assistant professor at Aalto University, Finland

Thang Bui

PhD student | 2013-2017

Thesis: Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models

Next: Lecturer in machine learning and data science at the University of Sydney

Alexandre Navarro

PhD student | 2013-2017

Thesis: Probabilistic Machine Learning for Circular Statistics: Models and inference using the Multivariate Generalised von Mises distribution

Next: Senior research scientist at AstraZeneca

Filipe Tobar

Postdoctoral researcher | 2014-2015

Next: Senior Lecturer in Machine Learning at Imperial College London

Rosy Southwell

Research assistant | 2013-2014

Next: PhD student at University College London