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Cambridge ELLIS unit

We are pleased to announce the creation of the Cambridge Unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), located in Cambridge, UK.


The University of Cambridge is a leading institution for research and education in Europe and a top player in the international league for machine learning and AI research.
The mission of the Cambridge ELLIS unit is to build on the excellent machine learning and AI infrastructure available within the University of Cambridge, and serve as a stepping stone towards creating a center of excellence that will:
  • Offer positions with outstanding academic freedom, visibility, and top packages.
  • Attract outstanding people that will serve as talent magnets.
  • Produce world-leading basic research in machine learning and AI.
  • Train the new machine learning and AI leaders in science and industry.
  • Engage in start-ups, secondments, and other activities that help society benefit from AI.


In the future, large-scale intelligent systems will support many of society's most important activities including medicine, transportation, commerce, finance, science and engineering. These systems will provide large-scale data-driven automated decision making that will underpin the efficient and effective operation. The focus of the unit will be on advancing the field of machine learning and AI to enable these future intelligent systems.
"The industry and the university in Cambridge are both animated with the excitement of rapid progress in learning and intelligent systems. ELLIS links this vibrant multidisciplinary community to other centres of excellence across Europe, creating a unique partnership for creativity and innovation in the field. Cambridge University Department of Engineering is proud to be a part of this remarkable collaboration."
Prof. Richard Prager
Head of the Department of Engineering, University of Cambridge
"I am delighted at the establishment of the Cambridge ELLIS Unit which unites local industry and academic excellence in Artificial Intelligence. AI is becoming intrinsic to a fair and prosperous society for all. ELLIS forges strong links across Europe enabling collaborative progress in areas permeating our communities."
Daniel Zeichner MP
Member of Parliament for Cambridge


The Cambridge ELLIS unit was made possible with the generous donations from the following Cambridge companies:


Bill Byrne

Bill is Professor of Information Engineering, a Fellow at the Alan Turing Institute, and an Amazon Scholar. He has been Head of Information Engineering in the University of Cambridge, and he was the founding Director of the Cambridge MPhil in Machine Learning and Machine Intelligence. His research is in the statistical modelling of speech and language with particular interests in machine translation, dialogue systems, probabilistic automata, and the ethics of AI in communications technology. His research has received funding from Google, Microsoft, IBM, Toyota, DARPA (USA), NSF (USA), EPSRC (UK), EC (FP7) and he has extensive experience in the commercialisation of language technology.

Gábor Csányi

Gábor is Professor of Molecular Modelling. He is an expert in atomistic simulation, particularly in multi scale modelling that couples quantum mechanics to larger length scales. He is currently engaged in applying machine learning techniques to materials modelling problems: deriving force fields (interatomic potentials) from quantum mechanical calculations, designing similarity measures for molecules and atomic environments, predicting the binding of drug-like molecules to proteins. He is also interested in statistical problems in molecular dynamics. He is a founding editorial board member of the IOP journal Machine Learning: Science and Technology. He is an ELLIS fellow.

Zoubin Ghahramani

Zoubin is Chief Scientist and VP for AI at Uber, and Professor of Information Engineering. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence. He was a founding Cambridge Director of the Alan Turing Institute, the UK's national institute for data science and AI. His research focuses on probabilistic approaches to machine learning and artificial intelligence. He was co-founder of Geometric Intelligence (now Uber AI Labs) and advises a number of AI and machine learning companies. In 2015, he was elected a Fellow of the Royal Society for his contributions to machine learning. He is a member of the ELLIS society.

Mark Girolami

Mark was elected to the Sir Kirby Laing Chair of Civil Engineering. He holds the Royal Academy of Engineering Research Chair in Data Centric Engineering. His research lies at the intersection of the Statistical, Mathematical, and Computing Sciences. He was a founding Executive Director of the Alan Turing Institute and currently is Programme Director in Data-Centric Engineering. He is an elected fellow of the Royal Society of Edinburgh, EPSRC Advanced Research Fellow (2007–2012), EPSRC Established Career Research (2012–2018) Fellow, & received a Royal Society Wolfson Research Merit Award. He advises the UK Government Ministry of Defence on their Defence Science Committee and is Editor-in-Chief of Statistics & Computing.

José Miguel Hernández Lobato

Miguel is a University Lecturer in Machine Learning, Visiting Researcher at Microsoft Research Cambridge and Fellow at the Alan Turing Institute. Before, he did postdoctoral research at the University of Cambridge and at Harvard University. His research is in probabilistic machine learning, with interests in Bayesian deep learning, Bayesian optimization, automatic chemical design, reinforcement learning, and compression. His research has been used commercially by companies such as Infosys, Tencent, Siemens, Samsung, and Microsoft. He is a frequent Area Chair of UAI, IJCAI, ICML, AISTATS and AAAI and reviewer for NeurIPS, ICLR, and the Journal of Machine Learning Research. He is a member of the ELLIS society.

Neil Lawrence

Neil is Professor of Machine Learning, Senior AI Fellow at the Alan Turing Institute, visiting Professor at the University of Sheffield and the co-host of Talking Machines. His research interest is on machine learning through probabilistic models, focusing on both algorithms and applications with a focus on machine learning systems design. Neil was Associate Editor in Chief for IEEE PAMI (2011–2013) and is an Action Editor for the Journal of Machine Learning Research. He was the founding editor of the Proceedings of Machine Learning Research (2006) and is currently series editor. He is one of the founders of the Gaussian Process Summer School, the DALI Meeting and Data Science Africa and is a member of the UK's AI Council. He is a member of the ELLIS society.

Carl Edward Rasmussen

Carl is Professor of Machine Learning. He heads the Computational and Biological Learning Lab and the Machine Learning Group at the Engineering Department, and is Chief Scientist at, a Cambridge AI-company building a platform for decision-making. Before that, he was a Research Scientist at the Max Planck Institute for Biological Cybernetics in Tübingen. He obtained his Ph.D. from the University of Toronto under the supervision of Geoffrey Hinton. He has broad interests in probabilistic inference and decision making, including reinforcement learning, time series models, stochastic processes and modelling in control. His book with Chris Williams, Gaussian Processes for Machine Learning (2006), is a reference in the field. Carl is an ELLIS fellow and a fellow at the Turing Institute.

Mihaela van der Schaar

Mihaela is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, and Turing Fellow at the Alan Turing Institute. She has received the Oon Prize on Preventative Medicine from the University of Cambridge (2018). She has also been the recipient of an NSF Career Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards. She holds 35 granted USA patents. The current emphasis of her research is on developing new machine learning and AI methods for medicine and healthcare. She is an IEEE Fellow since 2010. She was identified by NESTA as the female AI researcher based in the UK with the most publications in the field. She is a member of the ELLIS society.

Richard E. Turner

Richard is a Reader in Machine Learning and Visiting Researcher at Microsoft Research Cambridge. He is Director of the Cambridge Machine Learning and Machine Intelligence MPhil programme and Co-Director of the UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks. He has received funding from Microsoft, Toyota, Google, DeepMind, Amazon, Improbable and EPSRC. He is on the Executive Committee for the Cambridge Centre for Data Driven Discovery and has been awarded the Cambridge Students' Union Teaching Award. His work has featured on BBC Radio 5 Live's The Naked Scientists, BBC World Service's Click and in Wired Magazine. He is an ELLIS fellow.

Adrian Weller

Adrian is a Principal Research Fellow in Machine Learning. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society, including scalability, reliability, interpretability and fairness. He is Programme Director for AI at the Alan Turing Institute, where he is also a Fellow. He is Principal Research Fellow at the Leverhulme Centre for the Future of Intelligence, the David MacKay Newton Research Fellow at Darwin College and an advisor to the Centre for Science and Policy. He serves on several boards, including the Centre for Data Ethics and Innovation. Previously, he held senior roles in finance. He is a member of ELLIS and assistant director of the ELLIS Programme on Human-centric Machine Learning.

Eiko Yoneki

Eiko is an Affiliated Lecturer and Senior Researcher and a Fellow at the Alan Turing Institute. Her research interests span distributed systems, networking and databases, including large-scale graph processing. Her current research focuses on auto-tuning of data processing/analytics frameworks to deal with complex parameter space using machine learning methods such as Structured Bayesian Optimisation and Reinforcement Learning. She received her Ph.D. degree from the University of Cambridge. Previously, she worked with IBM (US, Japan, Italy and UK).

Christian Steinruecken

Christian is a postdoctoral researcher at the Cambridge Machine Learning Group. He is interested in Bayesian inference, information theory, data compression, probabilistic programming, and automated model construction. He is the lead architect of the Automatic Statistician Project. Previously he worked with Prof Sir David J.C. MacKay, with whom he co-taught several courses. His research has been presented on BBC Radio 5 Live's The Naked Scientists, at the Royal Society in London, and at the United Nations AI for Good Global Summit. Christian is co-founder and CTO of Invenia Labs, and an advisor to several other companies. Christian is associate faculty at the Cambridge ELLIS unit.

Organisational structure

The Cambridge ELLIS unit is directed by Carl E. Rasmussen, and co-directed by José Miguel Hernández Lobato. Both are in charge of the management and direction of the unit, taking into account the vote of all the other members of the core faculty.
The operation, implementation and development of the unit will be performed by a coordination team, consisting of administrators, PhD students, postdoctoral researchers and part of the ELLIS core faculty.