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Global leaders named as Turing AI World-Leading Researcher Fellows


Five internationally-recognised researchers have been appointed as the first Turing AI World-Leading Researcher Fellows to conduct ground-breaking work on Artificial Intelligence’s (AI) biggest challenges.

The new fellows are:

  • Professor Zoubin Ghahramani, University of Cambridge
  • Professor Samuel Kaski, The University of Manchester
  • Professor Mirella Lapata, University of Edinburgh
  • Professor Philip Torr, University of Oxford
  • Professor Michael Wooldridge, University of Oxford

The fellowships, named after AI pioneer Alan Turing, are part of the UK's commitment to further strengthen its position as a global leader in the field.

Retaining and attracting some of the best international research talent in a highly competitive international environment will increase the UK’s competitive advantage and capability in AI.

The fellows’ research will have a transformative effect on the international AI research and innovation landscape by tackling some of the fundamental challenges in the field.

It could also deliver major societal impact in areas including decision-making in personalised medicine, synthetic biology and drug design, financial modelling, and autonomous vehicles.

The five Turing AI World-Leading Researcher Fellows are:

  • Professor Zoubin Ghahramani, University of Cambridge

Professor Ghahramani is Senior Director and Distinguished Researcher at Google, former Chief Scientist at Uber and a Fellow of the Royal Society.

In his fellowship, which he will hold jointly while continuing to work at Google, he aims to develop the new algorithms and applications needed to address limitations faced by the AI systems that underpin technologies such as voice and face recognition and autonomous vehicles. This includes ensuring they can better adapt to new data, and apply data-driven machine learning approaches to simulators to understand complex systems.


  • Professor Samuel Kaski, The University of Manchester

Professor Kaski holds a joint position as Research Director of the Christabel Pankhurst Institute for Health Technology Research, and as Director of the Finnish Centre for Artificial Intelligence at Aalto University.

Through his fellowship, Professor Kaski aims to overcome a fundamental limitation of current AI systems – that they require a detailed specification of the goal before they can help. In difficult design and decision tasks such as drug design we often cannot give that, because the desired outcomes uncertain and evolving. The new tools will be applied to help in drug design and to improve diagnoses and treatment decision making in personalized medicine.


  • Professor Mirella Lapata, University of Edinburgh

Professor Lapata is a Fellow of the Royal Society of Edinburgh, Director of the UKRI Centre for Doctoral Training in Natural Language Processing, holds the Royal Society Wolfson Merit Award and was the first recipient of the Karen Spärck Jones Award.

Professor Lapata aims to develop an AI system, inspired by the human brain, that is capable of advanced reasoning and able to draw conclusions from large and varied sets of data. This would address the limitations of current AI systems which cannot match the sophistication of the human brain in integrating large amounts of information from different sources.


  • Professor Philip Torr, University of Oxford

Professor Torr is a Fellow of the Royal Society and of the Royal Academy of Engineering, founder of spin-out Oxsight and a recipient of the Brian Mercer Award for Innovation and the Royal Society Wolfson Research Merit Award.

He aims to improve deep neural networks, which are widely used in applications from fraud-detection to self-driving cars but are vulnerable to being tricked by being fed incorrect information.

Through his fellowship Professor Torr will create a new centre of excellence to make deep learning reliable, robust and deployable as well as capable of efficiently handling the enormous quantities of data they will be fed with.


  • Professor Michael Wooldridge, University of Oxford

Professor Wooldridge is the Head of Computer Science at Oxford, Co-Programme Director for AI at The Alan Turing Institute, a recipient of the British Computer Society’s Lovelace Medal and a fellow of four AI and computing societies and associations.

Working with industrial partners Accenture Global Solutions, Facebook, JP Morgan, Oxford Asset Management, Schlumberger, and Vodafone, Professor Wooldridge aims to improve the agent-based AI models that are increasingly used in sectors such as financial modelling and logistics.

Professor Wooldridge’s team have previously used agent-based models to understand the causes of catastrophic collapses in global markets – so called

“flash crashes”, and they will continue this work in the project.


Professor Michael Wooldridge said: “Agent-based modelling is an old idea, but one that has really started to show promise recently. The fellowship will give me a unique opportunity to advance the science and technology of agent-based modelling along a broad range of directions.

“I will investigate languages that allow us to transparently capture agent-based models and the complex assumptions that they embody, so that these models can be more easily developed and understood.

“I'll investigate how models can be populated with realistic agent behaviours, which is essential if the models are to be of any value. Finally, I'll investigate how models can be calibrated and validated, so that we can have confidence in the predictions they make.”



Government Chief Scientific Adviser, Sir Patrick Vallance, commented:

“These five internationally-recognised researchers appointed as the first Turing AI World-Leading Researcher Fellows will help enable us to attract top talent from across the globe and ensure that the UK stays at the forefront of AI research and innovation.

“This expertise will increase the UK’s capabilities in AI and equip us to face greater and more complex challenges.”


The fellows are supported with a £18 million investment by UK Research and Innovation (UKRI).

In addition to this, 39 different collaborators including IBM, AstraZeneca and Facebook are making contributions worth £15.7 million to the fellows’ research programmes.

The fellowships are being delivered by UKRI’s Engineering and Physical Sciences Research Council.

EPSRC Executive Chair, Professor Dame Lynn Gladden, said:

“The Turing AI World-Leading Researcher Fellowships recognise internationally-leading researchers in AI, and provide the support needed to tackle some of the biggest challenges and opportunities in AI research.

“These fellowships enable the UK to attract top international talent to the UK as well as retaining our own world-leaders. Attracting and retaining top talent is essential to keep the UK at the leading edge of AI research and innovation.”

AI is a significant global opportunity to increase economic wealth and transform society, with many other countries investing heavily in research.

Today’s announcement is part of a £46 million investment in AI research leaders through Turing AI Fellowships which, alongside wider investment, is designed to build on the UK’s leading role in this field and boost its reputation as a great place to study, invest or work in AI.

The investment includes:

  • Five fellows awarded by The Alan Turing Institute in 2019;
  • Turing AI Acceleration Fellowships awarded by UKRI, announced last year with £20 million of funding.

The Turing AI Fellowships investment is be delivered in partnership by UKRI, the Office for AI, and The Alan Turing Institute, the national institute for data science and AI.



The Turing AI World-Leading Research Fellows:

Project Zoubin Ghahramani, University of Cambridge – Advancing Modern Data-Driven Robust AI

UKRI support: £2.6 million

Modern AI is dominated by methods that learn from large amounts of data and underpin technologies such as voice and face recognition, production recommendation and autonomous vehicles. They are also the basis of recent breakthroughs in AI like the game-playing systems that can beat humans at chess, Go and poker and underly practical advances in science, engineering and medicine such as automated tools for analysing genomic data and medical images.

Despite significant advances, these systems face a number of limitations. These include poor handling of large amounts of additional, useless information, uncertainty and changing circumstances, gaps in their ability to combine symbolic and statistical reasoning, and the lack of automation of many of the stages of learning.

Professor Ghahramani aims to develop new algorithms and applications to address these limitations, for example by ensuring that AI systems can better adapt to new data and report when they are unable to find a correct conclusion instead of continuing to an incorrect one. He aims to develop better tools to automate the process of building and maintaining machine learning systems and apply approaches from data-driven machine learning to simulators, which are widely used to model and understand complex systems in science and engineering. He aims to use the tools developed through his fellowship to address problems in modelling and optimising complex systems with many interdependent components, such as electrical grid efficiency and transportation systems.

Project Partners: Graphcore, Invenia Labs, Tractable Ltd, Wayve Technologies Ltd

Total Project Partner Contribution: £1.45 million


Professor Samuel Kaski, The University of Manchester: Human-AI Research Teams - Steering AI in Experimental Design and Decision-Making

UKRI support: £4.4 million

Machine learning, where solutions to problems are automatically learnt from data, is a form of AI with great promise for addressing a number of challenges. This includes healthcare, where AI can detect patterns associated with diseases and health conditions by studying healthcare records and other data. However, machine learning is still limited by the fact that we need to set appropriate objectives and rewards to tell AI systems which outcomes are desired. This is difficult when we only partially know the goal, as is the case at the beginning of scientific research.

Professor Kaski aims to develop new ways for machine learning systems to help humans in the problem solving process, of designing experiments, interpreting what results mean, and deciding what to measure next, to finally reach trustworthy solutions to problems. This new approach will be applied to three challenges: diagnosis and treatment decision-making in personalised medicine; the guidance of scientific experiments in synthetic biology and drug design; and the design and use of digital twins to design physical systems and processes. In drug design, for instance, the most advanced current tools are able to generate candidate molecules if we can specify a precise objective function for them. However, that is difficult for humans to do – and if our specifications are not perfect the intelligent system will very cleverly produce results we do not want. This is where the proposed new approaches and tools will help.

An AI centre of excellence will be established at The University of Manchester, in collaboration with The Alan Turing Institute and a number of partners from the industry and healthcare sector, and with strong connections to the networks of best national and international AI researchers.

Project Partners: Aalto University, Apis Assay Technologies Ltd, AstraZeneca, Delft University of Technology, Etsimo Healthcare Oy, Gendius Limited, Greater London Authority, Health Innovation Manchester, IBM, Kyoto University, Spectra Analytics, University of Birmingham, University of Cambridge, University of Toronto, Zero Carbon Farms Ltd, Greater Manchester Combined Authority and NHS through the Pankhurst Institute.

Total Project Partner contribution: £6.4 million


Professor Mirella Lapata, University of Edinburgh: TEAMER – Teaching Machines To Reason Like Humans

UKRI support: £3.9 million

Progress in deep learning, which aims to mimic the human brain to process information and make decisions, has led to advances in speech recognition, natural language processing and robotics. However AI systems utilising deep learning still suffer from drawbacks in reasoning, that is taking pieces of information, combining them and using them to draw logical conclusions or devise new information. While computer systems can draw conclusions from visual and textual information, the human brain is far more sophisticated at correlating and integrating information from different sources and re-using previous experience to transfer it to radically different challenges. Current AI systems fail when exposed to data outside the information they were trained on, adhering only to superficial and potentially misleading associations instead of learning true causal relationships while also being unable to reason on an abstract level and provide us with full understanding of how they came to a specific conclusion.

Professor Lapata aims to develop neural networks, inspired by the human brain, that are capable of advanced reasoning. The specialised components forming these networks would have differing strengths which would ensure they have a more well-rounded ability to reason, being able to draw conclusions from large and varied sets of data, deal with change, be creative and explain their predictions and decisions.

Project Partners: ARM Ltd, BBC, British Library, Google DeepMind UK, Dyson Limited, Huawei, IBM, Naver Labs Europe, RAS Technologies GMBH, Scottish and Southern Energy SSE plc, Brainnwave Ltd, Wallscope, Amazon Research Cambridge

Total Project Partner contribution: £4.2 million


Professor Philip Torr, University of Oxford – Robust, Efficient and Trustworthy Learning

UKRI support: £3 million

Deep neural networks imitate human intelligence and are capable of learning from huge amounts of data. They have a wide range of applications, from fraud detection and visual recognition to self-driving cars, but their limitations are becoming increasingly evident.

These include a vulnerability to adversarial examples, where data is presented with the intent of causing AI models to make mistakes. In safety-critical applications such as autonomous vehicles or medical diagnosis this presents issues around AI models incorrectly classifying information. For example, it has recently been demonstrated that the neural networks underlying autonomous vehicle autopilots can be fooled by markers on the ground into swerving into the opposite lane.

Through his fellowship Professor Torr will create a new centre of excellence to make deep learning reliable, robust and deployable as well as capable of efficiently handling the enormous quantities of data they will be fed with.

Project Partners: Five AI, Facebook (International), Remark Holdings

Total Partner Project contribution:


Professor Michael Wooldridge, University of Oxford: The Large Agent Collider – robust agent-based modelling at scale

UKRI support: £3.5 million

Agent-based models are increasingly used in areas such as financial modelling, logistics, and supply chain management. Built from autonomous decision-making AI ‘agents’, they provide a novel way of modelling many systems. However current approaches to agent-based modelling have many limitations, including a lack of suitable modelling languages, and an inability to verify their predictions.

Professor Wooldridge aims to carry out the fundamental science needed to overcome these challenges, using state-of-the-art AI and machine learning techniques to develop, populate, calibrate, and validate agent-based models at scale. Working with major industrial partners, he will test and refine techniques on a range of real-world case studies to transform agent-based modelling from an ad hoc, trial-and-error process into a robust and trusted discipline.

Project Partners: JP Morgan Chase, Accenture Global Solutions Limited

Total Project Partner contribution: £60,000