Researchers from The Alan Turing Institute awarded Turing AI Acceleration Fellowships

Friday 27 Nov 2020

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A number of outstanding researchers from The Alan Turing Institute, the national institute for data science and artificial intelligence, have been chosen to develop AI technologies through prestigious fellowships announced today (Friday 27 November).

In all, 15 leading researchers from various UK institutes, including the Turing, have been awarded Turing AI Acceleration Fellowships, named after AI pioneer Alan Turing. The fellowships are supported by a £20million government investment, being delivered by UK Research and Innovation (UKRI) to lead innovative, creative AI research with transformative impact.

The Turing AI Acceleration Fellowships will accelerate and support the careers of some of the brightest and most ambitious AI researchers, enabling them to become world-leading researchers over the five years of the award. This will sustain and strengthen the UK’s leading international position in AI. The novel AI techniques they will develop through the fellowships could have wide-ranging impact, for example through combatting cancer, developing digital twins that can aid with modelling and understanding air pollution, and applying trustworthy machine learning to areas such as healthcare and criminal justice. The successful researchers include Turing Fellows Dr Adrian Weller, Dr Theo DamoulasProfessor Christopher Yau, Professor Giovanni Montana, Dr Jose Miguel Hernandez Lobato.

Dr Adrian Weller, University of Cambridge, and the Turing’s Programme Director for AI: Trustworthy Machine Learning

Machine learning presents tremendous opportunities for society, but also introduces risks, such as embedding unfair biases or creating new vulnerabilities. It is therefore crucial that we can understand and deliver what is needed for such systems to be trustworthy. Dr Weller’s work will build solid technical underpinnings for trustworthy machine learning via new theory and practical algorithms, focusing on three key measures: fairness, interpretability and robustness. The work will examine applications in criminal justice and healthcare – two domains with high-stakes decisions and clear outcome goals where algorithms are rapidly being introduced – and will help strengthen the UK’s position as a world leader in responsible AI.

Dr Weller said, “I am thrilled to be awarded this fellowship to help build the foundations for the trustworthy deployment of machine learning systems to benefit society. This work fits well with The Alan Turing Institute’s agenda on safe and ethical AI and other programmes. Machine learning presents great hope to improve outcomes, but also raises important concerns for individuals and society which I will help to address in critical application settings, engaging with expert partners.”

Dr Theo Damoulas, University of Warwick and the Turing’s Deputy Programme Director for Data-Centric Engineering: Machine Learning Foundations of Digital Twins

Dr Damoulas aims to establish the machine learning foundations for AI-enabled digital twins: digital representations of objects and systems that are tied to their physical ‘twins’ via data streams, and which allow researchers to simulate and evaluate ‘what if?’ scenarios to inform real-world decisions. The principles and advances from this research will be demonstrated in environmental and urban digital twins that will allow us to better understand and predict air pollution over cities, providing useful information for policy-making and mitigation.

Dr Damoulas said, “I am delighted to receive this prestigious fellowship and I am looking forward to establishing the machine learning foundations needed for next-generation, distributed and interacting digital twins.”

Professor Christopher Yau, The University of Manchester, Co-Director of the Health Data Research UK-Turing Wellcome PhD Programme in Health Data Science: clinAIcan – Developing Clinical Applications of Artificial Intelligence for Cancer

Professor Yau aims to develop AI-driven predictive models that allow us to describe how cancers evolve at the molecular level. He aims to exploit the fact that cancers, whilst never exactly identical, often share similar development trajectories. We can learn about these by collating copious information from high-resolution molecular profiles of many cancers. By embedding the biology of cancer within AI models, Yau will develop intelligent systems that can produce realistic and interpretable predictions for the progression of cancers. This will help to improve the efficiency of drug development and decisions on treatment, and provide patients with more information about their illness.

Professor Yau said, "I am very excited to have been awarded this Fellowship which will enable me to conduct ground-breaking research at the intersection of genomics and artificial intelligence. Genomics will yield unprecedented amounts of data which necessitate the use of AI for their interpretation.

“I will be developing novel clinical information systems to provide cancer patients and clinicians with the very best genomics-guided personalised care to improve treatment effectiveness and survival rates. I am especially pleased to be working with a range of project partners, including Ovarian Cancer Action, to ensure that my research is conducted in partnership with patients."

The Turing AI Fellowships are delivered through UKRI’s Engineering and Physical Sciences Research Council (EPSRC), in partnership with the Department for Business Energy and Industrial Strategy, Office for AI and The Alan Turing Institute. National cohort management will be led by UKRI, in partnership with the Office for AI and the Turing.

These fellowships will increase collaboration between academia and industry, with each fellow bringing together a wide range of partners on their projects to accelerate the impact of their transformative AI technologies. Partners have already committed to cash and in-kind contributions in excess of £10m. The Turing AI Acceleration Fellowships are part of the investment in Turing AI Fellowships, which was announced in the 2018 Budget following the government’s review of the UK AI industry.

Five fellowships have previously been awarded and the Turing AI World-Leading Researcher Fellowships call is in progress. These fellowships are part of a major government investment in AI skills and research which also includes 16 UKRI Centres for Doctoral Training in AI announced by Prime Minister Boris Johnson.

Further information: In full, the Turing AI Acceleration Fellows are:

  • Professor Damien Coyle, University of Ulster – AI for Intelligent Neurotechnology and Human-Machine Symbiosis
  • Dr Jeff Dalton, University of Glasgow – Neural Conversational Information Seeking Assistant
  • Dr Theo Damoulas, University of Warwick – Machine Learning Foundations of Digital Twins
  • Professor Aldo Faisal, Imperial College – Reinforcement Learning for Healthcare
  • Professor Yulan He, University of Warwick – Event-Centric Framework for Natural Language Understanding
  • Dr Jose Miguel Hernandez Lobato, University of Cambridge – Machine Learning for Molecular Design
  • Dr Antonio Hurtado, University of Strathclyde – PHOTONics for Ultrafast Artificial Intelligence
  • Dr Per Lehre, University of Birmingham – Rigorous Time-Complexity Analysis of Co-evolutionary Algorithms
  • Professor Giovanni Montana, University of Warwick – Advancing Multi-Agent Deep Reinforcement Learning for Sequential Decision Making in Real-World Applications
  • Dr Christopher Nemeth, Lancaster University – Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL)
  • Dr Raul Santos-Rodriguez, University of Bristol – Interactive Annotations in AI
  • Dr Sebastian Stein, University of Southampton – Citizen-Centric AI Systems
  • Dr Ivan Tyukin, University of Leicester – Adaptive, Robust and Resilient AI Systems for the FuturE
  • Dr Adrian Weller, University of Cambridge – Trustworthy Machine Learning
  • Professor Christopher Yau, The University of Manchester – clinAIcan – Developing Clinical Applications of Artificial Intelligence for Cancer