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About Us

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.

The Accelerate Programme for Scientific Discovery will advance the frontiers of science through the application of AI.

Supported by a donation from Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt, the Accelerate Programme will provide young researchers with specialised training in AI techniques, equipping them with the skills they need to use machine learning and AI to power their research.

By pursuing an ambitious research agenda that applies machine learning to the scientific challenges of the 21st century, the Programme will generate insights that accelerate scientific progress and create AI tools that are capable of delivering benefits for science and society.

Our team

Neil D. Lawrence

Professor of Machine Learning

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge. His main interest is the interaction of machine learning with the physical world. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.

Jessica Montgomery

Executive Director

Jessica is Executive Director of the Accelerate Programme for Scientific Discovery. She is also Director of the Data Trusts Initiative, a project tackling the actions needed to create trustworthy data governance frameworks. Her interests in AI and its consequences for science and society stem from her policy career, in which she worked with parliamentarians, leading researchers and civil society organisations to bring scientific evidence to bear on major policy issues.

Carl Henrik Ek

Senior Lecturer

Carl Henrik is a senior lecturer at the University of Cambridge. His research is focused on building sound statistical models that are applicable to real world data. Most of his work to date have been on Bayesian non-parametric models which allows for principled treatment of uncertainty, easy interpretability and adaptable complexity. He is interested in the developing field of probabilistic numerics and has worked on reinforcement learning and Bayesian optimisation.


Accelerate Programme Coordinator

The Accelerate Programme is an interdisciplinary research team using machine learning to advance the frontiers of science. It is based in Cambridge University’s Department for Computer Science and Technology. Our Programme Coordinator supports the team's activities in research, education and learning, and events and engagement, and can help respond to queries about forthcoming Programme activities. To contact the Programme Coordinator, please email accelerate-science@cl.cam.ac.uk

Sarah Morgan

Departmental Early Career Academic Fellow

Sarah's research applies machine learning, network science and Natural Language Processing to better understand and predict mental health conditions. A main focus is using brain connectivity derived from MRI to predict disease trajectories for patients with schizophrenia. Sarah is also interested in using transcribed speech data to perform similar prediction problems.

Challenger Mishra

Departmental Early Career Academic Fellow

Challenger is a Theoretical Physicist working on the problem of Quantum Gravity. He is developing machine-driven approaches to problems in String Theory and related Calabi-Yau geometries, studying the vast landscape of String Theory solutions using a combination of tools and techniques from machine learning and mathematical physics. These include differential and algebraic geometry, invariant theory, symbolic regression, and equivariant architectures. He is also developing new machine learning architectures and methodologies inspired by mathematical physics. These approaches will deepen understandings of the map between String Theory models and the Standard Model of particle physics.

Bingqing Cheng

Departmental Early Career Academic Fellow

Bingqing's research uses computer simulations to understand and predict material properties, with a particular focus on exploiting machine-learning (ML) methods to extend the scope of atomistic simulations. She has investigated phenomena and problems including interfaces, nucleation, crystal plasticity, nuclear quantum effects, crystal defects, and thermodynamic stabilities of materials.

Bianca Dumitrascu

Departmental Early Career Academic Fellow

Bianca works at the intersection of machine learning and genetics. Her main research interest is understanding how local molecular rules give raise to emergent spatial patterns in the context of biological dynamical systems. To this end, she uses techniques from statistical optimization, statistical physics and domain adaptation to identify contextual phenotypes in spatial transcriptomic data and to understand the identity of single cells and their interactions in early development. She is also interested in active learning and graphical neural networks as models to study the effects and side-effects of drug cocktails.

Advisory Group

Ann Copestake

Head of the Department of Computer Science and Technology

Mark Girolami

Professor of Computing and Inferential Science

Austen Lamacraft

Professor of Theoretical Physics

Neil D. Lawrence

Professor of Machine Learning

Carola-Bibiane Schönlieb

Professor of Applied Mathematics and Head of the Cambridge Image Analysis (CIA) Group

Sarah Teichmann

Director of Research, Department of Physics, and Head of Cellular Genetics, Wellcome Sanger Institute

Richard Turner

Professor of Machine Learning