UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing


The Postgraduate in Artificial Intelligence Link (PAI-Link) brings together PhD students in Machine Learning, Artificial Intelligence and Data Science across the country.

2020 cohort

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Name University Project Title Theme Supervisor(s)
Luke Ian Lunn Aberystwyth Approximating the colour of Mars T1, T3 Helen Miles
Bishnu Paudel Aberystwyth Automatic stroke recovery prediction using artificial intelligenceT2 Otar Akanyeti, Reyer Zwiggelaar
Will Robinson Aberystwyth Detecting when deep learning goes wrong in medical image analysisT2 Bernie Tiddeman, Reyer Zwiggelaar
Franciszek KrzyzowskiBangor Learning from badly behaving dataT3 Lucy Kuncheva, Franck Vidal
Iwan MitchellBangor Automated optimisation of industrial X-ray computed tomography T3 Franck Vidal, Simon Middleburgh
Jake AmeyBristol New Physics searches in B and D meson decays with machine learningT1 Jonas Rademacker, Konstantinos Petridis
Matthew SelwoodBristol Using machine learning to explore the evolution of active galaxies with Euclid T1 Sotiria Fotopoulou, Malcolm Bremer
Drew BarrattCardiff Examination of SARS-CoV-2 severity, transmissibility and spread within Wales through the analysis of linked patient health records and genomic sequence dataT3 Tom Connor
Matthew WalkerCardiff Inferring brain tissue microstructure from standard structural imaging T2 Leandro Beltrachini, Kevin Murphy
Samuel WincottCardiff AI and neuro-evolution: Exploiting network motifs to enhance prediction of contagion in complex networksT3 Roger Whitaker, Alun Preece
Natalia SikoraSwansea Enhancing the diagnostic performance of a bowel cancer blood test using advanced machine learning algorithms and the incorporation of information from the patient's medical recordT2 Peter Dunstan, Dean Harris
Lukas GolinoSwansea Machine learning with anti-hydrogenT1 Niels Madsen, Gert Aarts
Maciej Glowacki*Bristol Searches for Beyond-Standard-Model signatures with jets + missing energyT1 Henning Flaecher
Jacob Elford* Cardiff Monsters in the dark: gas, dust and star formation around supermassive black holesT1 Timothy A. Davis, Mattia Negrello
David Mason*Swansea Non-perturbative dynamics and compositenessT1 Biagio Lucini, Maurizio Piai
Jack Furby**Cardiff Human-machine collaboration with deep learning agentsT3 Alun Preece
Paul Murphy**Cardiff Adaptive neural networks through epigenetic processesT3 Roger Whitaker
Ben Page**Swansea Studies of thermal QCD using lattice gauge theoryT1 Chris Allton

*STFC CDT on Data-Intensive Science
**Associate member

2019 cohort

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Name University Project Title Theme Supervisor(s)
Lily Major Aberystwyth Big Data algorithmics for efficient search and analysis of large collections of genomes T2 Amanda Clare, Jacqueline Daykin, Benjamin Mora, Christine Zarges
Cory Thomas Aberystwyth Modelling the development of breast cancer abnormalities T2, T3 Reyer Zwiggelaar, Tom Tornsey-Weir, Jason Xie
Vanessa Cassidy Bangor Machine learning for narrative data visualisation T3 Jonathan Roberts, Panos Ritsos
Benjamin Winter Bangor The research of neuroevolution algorithms T3 William Teahan, Franck Vidal
Hattie Stewart Bristol AI techniques for extracting source information from Square Kilometre Array (SKA) datasets T1 Mark Birkingshaw
Robbie Webbe Bristol X-Ray Astronomy, concerning the identification and classification of highly variable AGN T1 Andy Young
Christopher Wright Bristol Multi-channel waveform reconstruction for dark matter searches with LUX-ZEPLIN T1 Henning Flaecher, Stephen Fairhurst
Michael Norman Cardiff Deep learning for real-time gravitational wave detection T1 Patrick Sutton
Bradley Ward Cardiff Investigating the epoch of galaxy formation using artificial intelligence T1 Steve Eales
Tonicha Crook Swansea Game theory T3 Arno Pauly, Edwin Beggs
Jamie Duell Swansea Machine learning in medical science T2 Xiuyi Fan, Shangming Zhou, Gert Aarts
Sophie Sadler Swansea Visual analytics for explainable graph-based machine learning T3 Daniel Archambault, Mike Edwards
Raul Stein* Bristol FPGA implementation of machine learning for low latency data processing in particle detectors T1 Jim Brooke
Eleonora Parrag* Cardiff Rewinding supernovae with machine learningT1 Cosimo Inserra
Thomas Spriggs* Swansea Spectral features of hadronic states in thermal QCD T1 Chris Allton, Tim Burns

*STFC CDT on Data-Intensive Science