
Overview
By analysing complex datasets and uncovering previously unknown patterns, machine learning has the potential to accelerate scientific discovery across the sciences – from healthcare to climate science, fundamental physics to conservation, and more. Realising this potential requires action to equip researchers from across disciplines with the skills they need to use machine learning in their work, and to build a community of practice at the interface of data science and other disciplines.
How can AI enhance scientific discovery?
Activities in this theme advance research, training, and engagement at the interface of AI and the sciences.
Current areas of research interest include:
- innovations in Gaussian Processes, dimensionality reduction techniques and their application to challenges in the life sciences;
- the application of ideas from physics and applied mathematics to develop robust sampling methods;
- the development of computational methods for interrogating the relationship between cellular morphology and genomics;
- the application of data science approaches to better understand mental health conditions, including machine learning, network science and NLP;
- theoretical machine learning to understand String Theory;
- innovations in modelling and software engineering to support increased accuracy in climate predictions.
Projects

Human Cell Atlas
The Human Cell Atlas programme aims to chart the properties of human cells, building a reference map of the human body that can be used to understand human health and to treat disease.

Accelerate Science
Artificial intelligence (AI) has the potential to become an engine for scientific discovery across disciplines – from predicting the impact of climate change, to using genetic data to create new healthcare treatments, and from finding new astronomical phenomena to identifying new materials here on Earth.

Climate Ensembling
General Circulation Models (GCMs) of Earth's climate provide robust simulations of large-scale average climatic variables, such as end-of-century global average temperature, under various future greenhouse gas emissions scenarios. Translating these outputs to insights that can be used to manage the local-level impacts of climate change is challenging, and requires innovations in modelling, data management, and software engineering.
Related People

Aditya Ravuri
PhD Student, Cambridge University

Ahmad Abu-Khazneh
Senior Machine Learning Engineer, Accelerate Programme, Cambridge University

Bianca Dumitrascu
Departmental Early Career Academic Fellow, Accelerate Programme, Cambridge University

Carl Henrik Ek
Senior Lecturer, Cambridge University

Challenger Mishra
Departmental Early Career Academic Fellow, Accelerate Programme, Cambridge University

Diana Robinson
PhD Student, Cambridge University

Haoting Zhang
PhD Student, Cambridge University

Isaac Sebenius
PhD Student, Cambridge University

Jess Montgomery
Executive Director, Accelerate Science, Cambridge University

Justin Tan
PhD Student, Cambridge University

Katie Green
PhD Student, Cambridge University

Mala Virdee
PhD Student, Cambridge University

Neil D. Lawrence
The DeepMind Professor of Machine Learning, Cambridge University