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1. About the Turing AI Fellowships
The Turing AI Fellowships are a £46 million initiative created as part of the AI Sector Deal’s ambitious skills and talent package aimed at retaining, attracting and developing the best and brightest AI international researchers. Fellows will undertake world leading creative and innovative AI research, working in collaboration with partners from other sectors to accelerate the impact of their research.
The initiative consists of:
- an initial Turing AI Fellowships programme delivered by The Alan Turing Institute in 2019
- Turing AI Acceleration Fellowships aimed at accelerating the careers of high potential researchers developing next generation AI technologies, towards a world leading position by the end of the fellowship. Fellows were announced by UKRI in November 2020
- Turing AI World-Leading Researcher Fellowships intended to retain and recruit the best international researchers in AI to build new capability and capacity in the UK.
This further investment to double the number of Turing AI World-Leading Researcher Fellows was announced by Chancellor Rishi Sunak following the success of the first five fellows announced in 2021.
These prestigious fellowships will help to retain and attract some of the best international research talent in AI, as outlined in the National AI Strategy.
Leaders are able to apply for a Global Talent visa.
2. The Turing AI Fellows
The table lists the Turing AI Fellows supported by funding from the UK Government.
|Professor Damien Coyle||University of Ulster||Turing AI Acceleration Fellow||AI for Intelligent Neurotechnology and Human-Machine Symbiosis AI||Neurotechnology; Brain-computer interface (BCI); Electroencephalography (EEG); Rehabilitation||https://pure.ulster.ac.uk/en/persons/damien-coyle|
|Dr Jeff Dalton||University of Glasgow||Turing AI Acceleration Fellow||Neural Conversational Information Seeking Assistant||Conversational search; Neural-symbolic methods; Representation learning; Natural language understanding; Virtual personal assistants||www.dcs.gla.ac.uk/~jeff|
|Dr Theodoros Damoulas||University of Warwick||Turing AI Acceleration Fellow||Machine Learning Foundations of Digital Twins||Machine learning; Bayesian nonparametric; Causality; Distributed digital twins; Urban and environmental digital twins||https://warwick.ac.uk/fac/sci/dcs/people/theo_damoulas|
|Timothy Dodwell||University of Exeter||Turing AI Fellow||Intelligent Virtual Test Pyramids for High Value Manufacturing||Uncertainty Quantification; Probabilistic Machine Learning; Multiscale Modelling; Digital Twins; Data-centric Engineering||https://emps.exeter.ac.uk/engineering/staff/td336|
|Professor Aldo Faisal||Imperial College London||Turing AI Acceleration Fellow||Reinforcement Learning for Healthcare||Machine Learning; Reinforcement Learning; Digital twins; Digital Healthcare; AI for Medicine||www.imperial.ac.uk/people/a.faisal|
|Professor Yarin Gal||University of Oxford||Turing AI Fellow||Democratizing safe and robust AI through public challenges in Bayesian Deep Learning||Democratizing Safe; Robust AI|
|Professor Zoubin Ghahramani||University of Cambridge||Turing AI World-Leading Researcher Fellow||Advancing Modern Data-Driven Robust AI||Bayesian machine learning; Understanding deep learning; Scalable probabilistic programming; Simulation||Cambridge Machine Learning Group|
|Professor Yulan He||King’s College London||Turing AI Acceleration Fellow||Event-Centric Framework for Natural Language Understanding||Natural language understanding; Machine reading comprehension; Event graph learning, Biomedical QA; conversational QA||https://www.kcl.ac.uk/people/yulan-he|
|Dr Antonio Hurtado||University of Strathclyde||Turing AI Acceleration Fellow||PHOTONics for ultrafast Artificial Intelligence Neuromorphic Photonic Computing||Ultrafast AI Hardware; Energy efficiency; Defence & Security; Finance||www.strath.ac.uk/staff/hurtadoantoniodr|
|Professor Samuel Kaski||The University of Manchester||Turing AI World-Leading Researcher Fellow||Human-AI Research Teams - Steering AI in Experimental Design and Decision-Making||Probabilistic machine learning; User modelling; Automatic experimental design; AI-assisted decision making and design||https://www.research.manchester.ac.uk/portal/samuel.kaski.html|
|Dr Jose Miguel Hernandez Lobato||University of Cambridge||Turing AI Acceleration Fellow||Machine Learning for Molecular Design||Deep generative models of molecules; Molecule generation via chemical reactions; Data-efficient molecular optimization, Robust machine learning for molecule data; Accelerate drug and material design||https://jmhl.org|
|Professor Mirella Lapata||University of Edinburgh||Turing AI World-Leading Researcher Fellow||TEAMER – Teaching Machines To Reason Like Humans||Reasoning, Modularity, Generalization, Language understanding and generation||https://www.research.ed.ac.uk/en/persons/mirella-lapata|
|Neil Lawrence||University of Cambridge||Senior Turing AI Fellow||Innovation to Deployment - Machine Learning Systems Design||Deployed AI; Systems design; Data governance: AutoML: MLOps||https://www.cst.cam.ac.uk/people/ndl21|
|Dr Per Kristian Lehre||University of Birmingham||Turing AI Acceleration Fellow||Rigorous Time-Complexity Analysis of Co-Evolutionary Algorithms||Co-evolutionary algorithms; Algorithmic game theory; Analysis of algorithms; Combinatorial optimisation; Robust optimisation||www.cs.bham.ac.uk/~lehrepk|
|Professor Maria Liakata||Queen Mary University of London||Turing AI Fellow||Creating time sensors from language and heterogeneous user generated content||Natural language processing; Longitudinal methods; Change detection; Learning from heterogeneous data; Mental health||http://eecs.qmul.ac.uk/profiles/liakatamaria.html|
|Professor Giovanni Montana||University of Warwick||Turing AI Acceleration Fellow||Advancing Multi-Agent Deep Reinforcement Learning for Sequential Decision Making in Real-World Applications||Reinforcement learning; Decision making; Multi-agent systems; Digital healthcare; Manufacturing||https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1567|
|Dr Christopher Nemeth||Lancaster University||Turing AI Acceleration Fellow||Probabilistic Algorithms for Scalable and Computable Approaches to Learning (PASCAL)||Statistical machine learning; Probabilistic uncertainty; Scalable computing; Cybersecurity, Autonomous vehicles||www.lancs.ac.uk/~nemeth|
|Professor Raul Santos-Rodriguez||University of Bristol||Turing AI Acceleration Fellow||Interactive Annotations in AI||Human-centric machine learning; Annotations; Labels; Explainability; Digital health||www.raulsantosrodriguez.com|
|Anna Scaife||The University of Manchester||Turing AI Fellow||AI4Astro - AI for Discovery for Data Intensive Astrophysics||Bayesian Deep Learning; Data Intensive Astrophysics; Variational Inference; Uncertainty calibration; Reproducible AI||https://www.research.manchester.ac.uk/portal/anna.scaife.html|
|Dr Sebastian Stein||University of Southampton||Turing AI Acceleration||Fellow Citizen-Centric AI Systems Responsible AI||Multi-agent systems; Smart energy; Smart transportation; Disaster response||www.southampton.ac.uk/~ss1y07/ccais|
|Professor Philip Torr||University of Oxford||Turing AI World-Leading Researcher Fellow||Robust, Efficient and Trustworthy Learning||Computer vision; Deep learning; Robustness||https://eng.ox.ac.uk/people/philip-torr|
|Professor Ivan Tyukin||King’s College London||Turing AI Acceleration Fellow||Adaptive, Robust, and Resilient AI Systems for the FuturE||Robust, Stable, and Trustworthy AI and Machine Learning; Adaptive AI; Provably Certifiable AI; AI Error Correction; Learning and Generalisation in High Dimension||kclpure.kcl.ac.uk/portal/ivan.tyukin.html|
|Dr Adrian Weller||University of Cambridge||Turing AI Acceleration Fellow||Trustworthy Machine Learning||Fairness; Explainability; Robustness; Health; Justice||http://mlg.eng.cam.ac.uk/adrian|
|Professor Michael Wooldridge||University of Oxford||Turing AI World-Leading Researcher Fellow||The Large Agent Collider – robust agent-based modelling at scale||Agent-based modelling; Multi-agent systems; Model verification; Model validation; Model calibration||https://www.cs.ox.ac.uk/people/michael.wooldridge|
|Professor Christopher Yau||University of Oxford||Turing AI Acceleration Fellow||clinAIcan - Developing clinical applications of artificial intelligence for cancer||Probabilistic modelling; Deep learning; Causal inference; Cancer; Genomics||https://www.bdi.ox.ac.uk/Team/christoper-yau|
3. Connect with a Turing AI Fellow
The Turing AI Fellowships are intended to enhance connections between academia and industry through supporting cross-sector collaborations and enabling the two-way flow of knowledge and people. This will accelerate the impact of the world-leading AI research being carried out by the fellows and benefit UK industry through the creation of new academic-industry collaborations and partnerships.
Please contact the fellows directly if you’re interested in finding out more about their research programmes.
General enquiries about the Turing AI Fellowships investment may be sent to AI.firstname.lastname@example.org.
4. The Turing AI Acceleration Fellowships
The Turing AI Acceleration Fellowships were awarded with the aim of accelerating the careers of fifteen high potential mid-career researchers towards a world-leading position by the end of the five year award. They are all delivering a high quality programme of ambitious, novel and creative research, in an area of AI of opportunity for the UK, with world leading results.
The Turing AI Acceleration Fellowships will:
- position the UK Internationally in AI Research and Innovation, attracting further talent and inward investment by signalling the UK’s ambition to be a global leader in the development of AI and enhance our research power in the field
- build new UK capability and maintaining the resilience of the academic research base by investing in the recruitment and retention of world-leading and high-potential researchers, enabling blue skies research and training of future talent
- support a diverse research community in the high profile growing area of AI thereby ensuring a diverse and sustainable ecosystem of AI leaders, with a strong future pipeline of talent
- enable new models of collaboration and career paths across sectors in AI — greater porosity between academia and industry will retain researchers in academia, create collaborations and enable the two-way flow of knowledge and people between sectors
- integrate consideration of AI adoption into AI research activities by embedding high expectations of the development of safe, ethical and responsible AI technologies at all stages of research
5. Turing AI World-Leading Researcher Fellowships
The prestigious Turing AI World-Leading Researcher Fellowships have been awarded to five world-leading AI researchers, with significant packages of support to enable the building of centres of excellence in key areas of AI research. The awards will allow some of the brightest minds in AI to move to (or remain in) the UK, whilst maintaining the momentum of their research programmes thereby building and growing the UK’s international competitiveness and reputation in AI.
The new fellows, who will conduct ground-breaking work on AI’s biggest challenges, are:
- Professor Zoubin Ghahramani, University of Cambridge
- Professor Samuel Kaski, The University of Manchester
- Professor Mirella Lapata, The University of Edinburgh
- Professor Philip Torr, University of Oxford
- Professor Michael Wooldridge, University of Oxford
The fellows will:
- establish a world leading centre of excellence in a strategically important area of AI research
- lead a major programme of AI research, translation and innovation
- build strong relationships and collaborations with academia, industry and broader stakeholders in the UK and internationally
- act as a leader in the community and as an ambassador and advocate for it, driving forward the UK and international research agenda
- develop the skills and careers of their teams, developing the independent researchers and innovators of the future
- actively engage with the questions around AI and ethics, and responsible research and innovation (RRI) throughout their activities
- deliver research with a high likelihood of impact on UK society and the economy
- build a broader portfolio of funding and activities beyond the fellowship, moving towards a position of sustainability at the end of the fellowship