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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

The DeepMind Professor of Machine Learning

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the physical world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end’ solutions are normally required. 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.

Jess Montgomery

Executive Director, Accelerate Science

Jess is Executive Director of the Accelerate Programme for Scientific Discovery, and Director of the Data Trusts Initiative. She has a range of collaborations in areas where AI is being used to tackle real-world challenges. These explore the roles that technological advances, scientific evidence, policy development and public dialogue can play in sharing the benefits of AI technologies across society. Her interests in AI and its consequences for science and society stem from her policy career, in which she worked with senior 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 in Machine learning in the Department of Computer Science and Technology at the University of Cambridge. Learning is the task of creating structure of uncertainty by making assumptions of the world. The science of machine learning is concerned with how to formulate assumptions into mathematics (modelling) and how to related them to observed data (inference). Carl Henrik's research spans both these areas, in specific he is interested in how we can create data efficient and interpretable assumptions that allows us to learn from small amounts of data. Before joining the group in Cambridge Carl Henrik was a Senior Lecturer at the University of Bristol, prior to this he was an Assistant Professor at the Royal Institute of Technology (KTH) in Stockholm. He did my postdoctoral research at University of California at Berkeley and his PhD is from Oxford Brookes University. His undergraduate degree is a MEng degree in Vehicle Engineering from the Royal Institutie of Technology in Stockholm.

Ahmad Abu-Khazneh

Senior Machine Learning Engineer, Accelerate Programme

Ahmad is interested in developing tools that help scientists in various disciplines utilise machine learning in their research. This is inspired by his cross-disciplinary academic and industrial background in lecturing on and implementing machine learning techniques. Most recently at Imperial College London he worked on using Natural Language Processing techniques to enhance assistive devices used by people with Motor Neurone Disease.

Sarah Morgan

Departmental Early Career Academic Fellow, Accelerate Programme

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.

Bianca Dumitrascu

Departmental Early Career Academic Fellow, Accelerate Programme

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.

Challenger Mishra

Departmental Early Career Academic Fellow, Accelerate Programme
Department of Computer Science and Technology

Challenger 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. His work seeks to deepen understandings of the map between String Theory models and the Standard Model of particle physics.

Aditya Ravuri

PhD Student

Aditya is interested in probabilistic machine learning, specifically in situations where there is interesting prior information available. As Gaussian processes routinely lend themselves to such applications, he enjoys working with them. He is also interested in finding a statistical basis for some classical algorithms.

Justin Tan

PhD Student

Justin's research lies at the intersection of geometry and machine learning, searching for interesting structure in geometries which feature in string theory and other areas of mathematical physics. Previously he worked in experimental particle physics at the Belle II experiment.

Katie Green

PhD Student

Katie is a PhD student and member of the AI4ER CDT and is supervised across Computer Science and British Antarctic Survey. She is interested in the application of machine learning in ecology and how different methodologies can be leveraged to learn about the underlying dynamics of ecosystems.

Isaac Sebenius

PhD Student

Isaac is interested in developing new computational methods that leverage biological knowledge to address open questions related to mental health and psychiatric disorders. In particular, Isaac’s work seeks to characterize and predict the spectrum of psychotic disorders by using machine learning to combine multiple types of neuroimaging-derived brain connectivity as well as genetic and other biological data.

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

The DeepMind 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