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

Lead Research Organisation: Swansea University
Department Name: College of Science


We live in a society dominated by information. The collection of data is an ongoing and continuous process, covering all aspects of life, and the amount of data available in recent years has exploded. In order to make sense of this data, utilise it, gain insights and draw conclusions, new computational methods to analyse and infer have been developed. This is often described by the general term "artificial intelligence" (AI), which includes "machine learning" or "deep learning", which rely on the processing of information by computers to extract nontrivial information, without providing explicit models. Highly visible are developments driven by social media, as this affects every person in a very explicit manner. However, AI is widely adopted across the industrial sectors and hence underpins a successful growth of the UK's economy. Moreover, also in academic research AI has become a toolset used across the disciplines, beyond the traditional realms of computer and data science. Research in science, health and engineering relies on AI to support a wide range of activities, from the discovery of the Higgs boson and gravitational waves via the detection of breast cancer and diabetic retinopathy to autonomous decision- making and human-machine interaction.

In order to sustain the industrial growth, it is necessary to train the next generation of highly-skilled AI users and researchers. In this Centre for Doctoral Training, we deliver a training programme for doctoral researchers covering a broad range of scientific and medical topics, and with external partners engaged at every level, from large international companies via government agencies to SMEs and start-ups. AI relies on computing and with data sets growing ever larger, the use of advanced computing skills, such as optimisation, parallelisation and scalability, becomes a necessity for the bigger tasks. For that reason the CDT has joined forces with Supercomputing Wales (SCW), a new £15 million national supercomputing programme of investment, part-funded by the European Regional Development Fund. The CDT will connect researchers working at Swansea, Aberystwyth, Bangor, Cardiff and Bristol universities with regional and national industrial partners and with SCW. Our CDT is therefore ideally placed to link AI and high-performance computing in a coordinated fashion.

The academic foundation of our training programme is built on research excellence. We focus on three broad multi- disciplinary scientific, medical and computational areas, namely
- data from large science facilities, such as the Large Hadron Collider, the Square Kilometre Array and the Laser Interferometer Gravitational-Wave Observatory;
- biological, health and clinical sciences, including access to electronic health records, maintained in the Secured Anonymised Information Linkage databank;
- novel mathematical, physical and computer science approaches, driving future developments in e.g. visualisation, collective intelligence and quantum machine learning.
Our researchers will therefore be part of cutting-edge global science activities, be able to modernise public health and determine the future landscape of AI.
We recognise that AI is a multidisciplinary activity, which extends far beyond single disciplines or institutions. Training and engagement will hence take place across the universities and industrial partners, which will stimulate interaction. Ideally, a doctoral researcher should be able to apply their skills on a research topic in, say, health informatics, particle physics or deep learning, and be able to contribute equally.

To ensure our training is aligned with the demands from industry, the CDT's industrial partners will co-create the training programme, provide input in research problems and highlight industrial challenges. As a result our researchers will grow into flexible and creative individuals, who will be fluent in AI skills and well-placed for both industry and academia.

Planned Impact

Driven by the doctoral researchers, our Centre will deliver impactful projects, underpinned by excellent science, with a strong backing from industry and wide connections with society. Through coordinated activities, our researchers play a central role in enabling measurable and actionable impact on people, knowledge, economy and society, directly linked to the UK Industrial Strategy, and the grand challenges of Artificial Intelligence and Big Data, both at the national and the international level.

Impact will be first and foremost on the Centre's doctoral researchers, who will be trained to produce and consume novel and state-of-the-art AI and HPC/HPDA techniques, the latter being a distinctive and essential component of our training programme. They will develop and mature as a workforce with unique skills that will advance knowledge, the economy and society. Our researchers will benefit from expert training on public engagement and outreach, which will enable them to provide a fresh insider perspective on current debates surrounding AI, its advantages and potential ethical issues targeting wide audiences.

Through the research projects and engagement with industry, the Centre will significantly advance the development and use of novel AI techniques applied to data from large science facilities and biological, health and clinical sciences. This will be obtained through synergies among the three thematic areas, in which algorithmic advances are driven by scientific needs and significance of practical applications, impacting both AI as a sector and the specific application areas. In addition, whenever possible, we will apply methods originally developed for one science application to others, enabling cross-fertilisation and interdisciplinarity. The primary impact here will be on researchers working in the thematic areas of the Centre and companies working in the same economic segments as the industrial partners of the Centre, together with partner government agencies and laboratories.

Our Centre has been co-created with a wide range of external partners, who will work directly with doctoral researchers, either on industry-led research projects or through secondments, obtaining a direct benefit as a return for their investment in the Centre. Being trained and developed as the next generation of data science specialists for real-world industrial problems requiring HPC/HPDA informed AI methodologies, our researchers will impact the economy and society substantially, through their future employments in skilled jobs in AI-enabled industrial practices, introducing AI in new industrial sectors, and potentially starting their own companies powered by their research findings.

In the theme of health, our Centre will contribute to societal and economic impact, by supporting novel and actionable precision medicine and public health approaches, contributing to the improvement of disease diagnosis, prevention and management, helping policy-makers develop efficient intervention strategies for targeted groups of people, and informing the future configuration of healthcare, directly impacting all stakeholders, from the NHS to policy makers and the general public.

Finally, our Centre brings together some of the big science questions, on the origin of the universe, the evolution of stars and the fundamental laws of nature, with urgent questions for humankind, on health, resilience and healthier lifestyles, via the development of new AI methods to analyse data intelligently. By actively contributing to showcases and debates on these topics, we will create unique and exciting opportunities to engage with the general public and policy makers, and increase the awareness and understanding of scientific, public health, and AI issues.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023992/1 31/03/2019 29/09/2027
2281471 Studentship EP/S023992/1 30/09/2019 30/03/2022 Vanessa Claire Cassidy
2265644 Studentship EP/S023992/1 30/09/2019 31/12/2023 Jamie Duell
2286560 Studentship EP/S023992/1 30/09/2019 29/09/2023 CORY THOMAS
2278233 Studentship EP/S023992/1 30/09/2019 28/02/2020 Benjamin Loki Hughes
2282975 Studentship EP/S023992/1 30/09/2019 29/09/2023 James Andrew Major
2269647 Studentship EP/S023992/1 30/09/2019 29/09/2023 Michael Norman
2265708 Studentship EP/S023992/1 30/09/2019 06/01/2024 Sophie Francesca Sadler
2296930 Studentship EP/S023992/1 30/09/2019 29/09/2023 Robbie Webbe
2265595 Studentship EP/S023992/1 30/09/2019 29/09/2023 Tonicha Marie Crook
2269669 Studentship EP/S023992/1 30/09/2019 29/09/2023 Bradley Ward
2281485 Studentship EP/S023992/1 30/09/2019 29/09/2023 Benjamin David Winter
2296807 Studentship EP/S023992/1 30/09/2019 29/09/2023 Christopher Wright
2296730 Studentship EP/S023992/1 30/09/2019 31/12/2023 Harriet Stewart
2431434 Studentship EP/S023992/1 30/09/2020 29/09/2024 Jake Amey
2430895 Studentship EP/S023992/1 30/09/2020 29/09/2024 Lukas Golino
2429404 Studentship EP/S023992/1 30/09/2020 29/09/2024 Matthew Ramsay Walker
2430831 Studentship EP/S023992/1 30/09/2020 29/09/2024 Natalia Sikora
2431105 Studentship EP/S023992/1 30/09/2020 29/09/2024 Iwan Mitchell
2431509 Studentship EP/S023992/1 30/09/2020 29/09/2024 Matthew Selwood
2414958 Studentship EP/S023992/1 30/09/2020 29/09/2024 Andrew Henry Mack
2431066 Studentship EP/S023992/1 30/09/2020 29/09/2024 Francis James Williams
2431364 Studentship EP/S023992/1 30/09/2020 29/09/2024 William George Robinson
2431345 Studentship EP/S023992/1 30/09/2020 29/09/2024 Bishnu Paudel
2431329 Studentship EP/S023992/1 30/09/2020 29/09/2024 Luke Ian Lunn
2429399 Studentship EP/S023992/1 30/09/2020 29/09/2024 Samuel Wincott
2596801 Studentship EP/S023992/1 30/09/2021 29/09/2025 Tabitha Grace Lewis
2596478 Studentship EP/S023992/1 30/09/2021 29/09/2025 Leena Sarah Farhat
2596361 Studentship EP/S023992/1 30/09/2021 29/09/2025 Laura Ballisat
2596387 Studentship EP/S023992/1 30/09/2021 29/09/2025 Myles Clayton
2601644 Studentship EP/S023992/1 30/09/2021 29/09/2025 Ding Sheng Ong
2596276 Studentship EP/S023992/1 30/09/2021 29/09/2025 Fergus Baker
2592736 Studentship EP/S023992/1 30/09/2021 29/09/2025 Luke Golby
2596788 Studentship EP/S023992/1 30/09/2021 26/12/2025 Zara Siddique
2595527 Studentship EP/S023992/1 30/09/2021 29/09/2025 Daniel Farmer
2596722 Studentship EP/S023992/1 30/09/2021 08/05/2026 Matthew Alan Powell
2596533 Studentship EP/S023992/1 30/09/2021 29/09/2025 Samuel Lee Hennessey
2737637 Studentship EP/S023992/1 30/09/2022 29/09/2026 Jamie Lea Pointon
2740485 Studentship EP/S023992/1 30/09/2022 29/09/2026 Michael Casaletto
2737565 Studentship EP/S023992/1 30/09/2022 29/09/2026 Preben Vangberg
2741117 Studentship EP/S023992/1 30/09/2022 29/09/2026 Tanya Kushwahaa
2741409 Studentship EP/S023992/1 30/09/2022 29/09/2026 Sama Al-Shammari
2741443 Studentship EP/S023992/1 30/09/2022 29/09/2026 Vasiles Balabanis
2740521 Studentship EP/S023992/1 30/09/2022 29/09/2026 Luke Williams
2740741 Studentship EP/S023992/1 30/09/2022 29/09/2026 Rhys Shaw
2740590 Studentship EP/S023992/1 30/09/2022 29/09/2026 Jamie Lea Pointon
2741418 Studentship EP/S023992/1 30/09/2022 29/09/2026 Chan Ju Park
2740611 Studentship EP/S023992/1 30/09/2022 29/09/2026 Preben Vangberg