Cambridge appoints first DeepMind Professor of Machine Learning
Following an international search, Professor Neil Lawrence has been appointed as the inaugural DeepMind Professor of Machine Learning at Cambridge, supported by a benefaction from the world-leading British AI company.
Professor Lawrence joins the University’s Department of Computer Science and Technology from Amazon Cambridge, where he has been Director of Machine Learning for the past three years. He is also Professor of Machine Learning at the University of Sheffield, where he will retain a visiting position.
Professor Lawrence’s research interests are in probabilistic models with applications in computational biology, personalised health and developing economies. At Sheffield, he led the ML@SITraN group, and helped to develop an Open Data Science Initiative an approach to data science designed to address societal needs.
Professor Lawrence completed his PhD at Cambridge’s Department of Computer Science and Technology in 2000. He has previously held positions at Microsoft Research Cambridge and the University of Manchester. In addition to his academic research, he hosts the Talking Machines podcast and is a contributor to the Guardian.
For the past five years, Professor Lawrence has been working with Data Science Africa, an organisation looking to connect machine learning researchers in Africa in order to solve problems on the ground. Professor Lawrence has an advisory role with the group, and says that many of the machine learning approaches used in Africa can have benefits in the developed world as well.
“With data and machine learning, you can have a more advanced data infrastructure in Africa than in some developed countries,” he said. “It’s rare in the UK or Europe that you’re asked to look at a machine learning problem from end to end, but you can do that in Africa, and it leads to better solutions. That’s the kind of approach I want to take to machine learning in my work at Cambridge.”
Dr Demis Hassabis (Queens' College 1994), co-founder and CEO, DeepMind, said: “I’m delighted to see Cambridge announce its first DeepMind Professor of Machine Learning. Professor Lawrence’s work in computational biology and his thoughtful advocacy for advancing technology in the developing world have been commendable. It’s an honour for DeepMind to be able to support the Department of Computer Science and Technology - from which I gained so much - in this way, and I look forward to seeing machine learning and AI flourish at Cambridge.”
“Neil will have a transformative effect on machine learning and artificial intelligence research at Cambridge,” said Professor Ann Copestake, Head of the Department of Computer Science and Technology. “He will build on our existing strengths in this area, and work with colleagues from across the University to develop new solutions in ethical and sustainable ways.”
“It is vital we have a deep pool of talented scientists in universities and industry so the UK can continue to be a world leader in artificial intelligence,” said Minister for Digital Mark Warman. “This Government is investing millions into skills and talent training, including a number of Turing AI Fellowships in partnership with The Alan Turing Institute, and I welcome the appointment of Professor Neil Lawrence as the inaugural DeepMind Professor of Machine Learning at Cambridge. This is one of a range of moves demonstrating the enormous strength of the UK’s research base.”
In addition to the gift to support the DeepMind Professorship, the company are also supporting four Master’s students from underrepresented groups wishing to study machine learning and computer science at Cambridge. The first students supported through this programme will be starting their studies this coming term.
There’s so much expertise at Cambridge, in all aspects of systems and data: that’s why I’m so excited about joining. AI and machine learning have the potential to reshape almost every aspect of our lives, but we desperately need more machine learning specialists, or else the promise of AI will not be realised.