UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing
PAI-Link
The Postgraduate in Artificial Intelligence Link (PAI-Link) brings together PhD students in Machine Learning, Artificial Intelligence and Data Science across the country.
2023 cohort
| Name | University | Project Title | Theme | Supervisor(s) |
| James Brimer | Aberystwyth | Anytime analysis for dynamic optimisation problems | T3 | Thomas Jensen |
| Seb Gregory Dal Toé | Aberystwyth | Multi-objective neuroevolution for energy efficient Deep Learning | T3 | Bernard Tiddeman |
| Nikola Nikolov | Aberystwyth | Analysing behaviour patterns to predict increasing risk of accidents in elderly | T2 | Patricia Shaw |
| Glory Ogbonda | Bangor | Visualisation of law using artificial intelligence | T3 | Pete Butcher |
| Rhiannon Owen | Bangor | AI Data visualisation and storytelling | T3 | Jonathan Roberts |
| Iwan Pritchard | Bangor | AI and Explanatory Visualisation | T3 | Jonathan Roberts |
| Shannon Jones | Bangor | Artificial Intelligence, Visualisation and IoT for Environmental Science | T3 | Noel Bristow, Panos Ritsos |
| Teresa Matamoro | Bristol | Coevolution of galaxies and supermassive black holes in the Euclid era | T1, T3 | Sotiria Fotopoulou |
| Jeronimo Segal | Bristol | Searching for New Physics with the CMS experiment at the LHC | T1 | Sudan Paramesvaran |
| Zak Williams | Bristol | Accelerating event simulation with machine learning for the LHCb experiment | T1 | Kostas Petridis |
| Natalia Zubova | Bristol | Machine Learning to find New Physics in muon decays | T1 | Joel Goldstein |
| Katherine Jiang | Cardiff | Predictive emergency service operations planning | T3 | Federico Liberatore |
| Uzuner Mete | Cardiff | A Cold and dusty Universe: Understanding the cosmic dust and cold gas in nearby galaxies | T1 | Matthew Smith |
| Thomas Rintoul | Cardiff | Simulations of massive galaxies and their circumgalactic medium | T1 | Freeke van de Voort |
| Aya Ghaleb | Swansea | Tests of the dark sector with gravitational waves | T1 | Ivonne Zavala |
| Diaa Eddin Habibi | Swansea | Using Machine Learning to understand lattice QCD data | T1 | Gert Aarts, Chris Allton |
| Rebecca Rees | Swansea | AI based approaches multi-dimensional functional genomics in cancer patients | T2 | Steve Conlan |
2022 cohort
| Name | University | Project Title | Theme | Supervisor(s) |
| Michael Casaletto | Aberystwyth | Prediction of facial growth for children with cleft lip and palate using 3D data mining and machine learning | T2, T3 | Richard Jensen |
| Luke Williams | Aberystwyth | Collaborative mapping of large scale outdoor environments | T3 | Myra Wilson |
| Preben Vangberg | Bangor | Automatically Analysing Big Language Data | T3 | William Teahan | Rhys Shaw | Bristol | Machine learning and radio source multiplicity | T1 | Mark Birkinshaw |
| Tanya Kushwahaa | Cardiff | Exploiting GAIA data and understanding the galaxies' past histories with machine learning | T1 | Mikako Matsuura |
| Sama Al-Shammari | Cardiff | Simulation-based Inference of gravitational waves signals from black holes and neutron stars | T1 | Vivien Raymond |
| Chanju Park | Swansea | Learning (from) lattice field theory | T1 | Gert Aarts, Biagio Lucini |
| Vasiles Balabanis | Swansea | Multimodal analysis of Anatomical and Functional features to enhance the understanding of Brain Processing Phenomena: A Machine Learning Approach. | T2, T3 | Scott Yang |
2021 cohort
| Name | University | Project Title | Theme | Supervisor(s) |
| Myles Clayton | Aberystwyth | A deep learning framework for agricultural plant breeding that predicts genotype-phenotype associations | T2 | Martin Swain, Chuan Lu |
| Ding Sheng Ong | Aberystwyth | Few-shot Learning for Environment Adaptive Multi-modal Vision System | T3 | Jungong Han |
| Leena Sarah Farhat | Bangor | Bringing big-data to social science | T3 | Simon Willcock, William Teahan |
| Dan Farmer | Bangor | Edge-based object recognition for immersive analytics in Web-based XR | T3 | Panagiotis (Panos) Ritsos |
| Sam Hennessey | Bangor | Ensembles of Deep Neural Networks for Semi-supervised Learning | T3 | Lucy Kuncheva |
| Fergus Baker | Bristol | Machine learning to study accretion flows around black holes | T1 | Andy Young |
| Laura Ballisat | Bristol | Advanced computational methods for dosimetry, planning and verification in emergent radiotherapy treatments | T1, T2 | Jaap Velthuis, Richard Hugtenburg (Swansea) |
| Matthew Powell | Cardiff | Real-time situational understanding using deep neural networks and knowledge graphs | T3 | Alun Preece |
| Zara Siddique | Cardiff | Evolving Ethical Deep Neural Networks | T3 | Roger Whitaker |
| Luke Golby | Swansea | AI based approaches multi-dimensional functional genomics | T2 | Steve Conlan |
| Tabitha Lewis | Swansea | ML-guided dynamical systems modelling of sepsis | T2, T3 | Noemi Picco |
| Shobhna Singh* | Cardiff | Dimer models on quasicrystals | T1 | Felix Flicker |
*Associate member
2020 cohort
| 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 intelligence | T2 | Otar Akanyeti, Reyer Zwiggelaar |
| Will Robinson | Aberystwyth | Detecting when deep learning goes wrong in medical image analysis | T2 | Bernie Tiddeman, Reyer Zwiggelaar |
| Francis Williams | Bangor | Learning from badly behaving data | T3 | Lucy Kuncheva, Franck Vidal |
| Iwan Mitchell | Bangor | Automated optimisation of industrial X-ray computed tomography | T3 | Franck Vidal, Simon Middleburgh |
| Jake Amey | Bristol | New Physics searches in B and D meson decays with machine learning | T1 | Jonas Rademacker, Konstantinos Petridis |
| Matthew Selwood | Bristol | Using machine learning to explore the evolution of active galaxies with Euclid | T1 | Sotiria Fotopoulou, Malcolm Bremer |
| Drew Barratt | Cardiff | Examination of SARS-CoV-2 severity, transmissibility and spread within Wales through the analysis of linked patient health records and genomic sequence data | T3 | Tom Connor |
| Matthew Walker | Cardiff | Inferring the brain tissue conductivity field from non-invasive imaging and machine learning | T2 | Leandro Beltrachini, Kevin Murphy |
| Samuel Wincott | Cardiff | Communication in multi-agent deep reinforcement learning | T3 | Roger Whitaker, Alun Preece |
| Natalia Sikora | Swansea | 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 record | T2 | Peter Dunstan, Dean Harris |
| Lukas Golino | Swansea | Machine learning with anti-hydrogen | T1 | Niels Madsen, Gert Aarts |
| Maciej Glowacki* | Bristol | Searches for Beyond-Standard-Model signatures with jets + missing energy | T1 | Henning Flaecher |
| Jacob Elford* | Cardiff | Monsters in the dark: gas, dust and star formation around supermassive black holes | T1 | Timothy A. Davis, Mattia Negrello |
| David Mason* | Swansea | Non-perturbative dynamics and compositeness | T1 | Biagio Lucini, Maurizio Piai |
| Jack Furby** | Cardiff | Human-machine collaboration with deep learning agents | T3 | Alun Preece |
| Paul Murphy** | Cardiff | Adaptive neural networks through epigenetic processes | T3 | Roger Whitaker |
| Ben Page** | Swansea | Studies of thermal QCD using lattice gauge theory | T1 | Chris Allton |
*STFC CDT on Data-Intensive Science
**Associate member
2019 cohort
| 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 |
| 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 learning | T1 | Cosimo Inserra |
| Thomas Spriggs* | Swansea | Spectral features of hadronic states in thermal QCD | T1 | Chris Allton, Tim Burns |
*STFC CDT on Data-Intensive Science