Dr Jay Morgan

Tutor, Computer Science

j.p.morgan@swansea.ac.uk

Basic Welsh Speaker

Admin - 015
Ground Floor
Computational Foundry
Bay Campus

Research Links

https://orcid.org/0000-0003-3719-362X

ResearchGate

Google Scholar

Scopus

About

Dr Jay Paul Morgan received his PhD from the Department of Computer Science, Swansea University. This work develops methods of how to combine expert domain knowledge with Deep Neural Networks in many scientific areas. Jay, through this PhD, has worked in the domains of: Quantum Chemistry, Linguistics, and Astrophysics.

Later, Jay held a post-doctoral position at the University of Toulon, France, as part of the CNRS-LIS Laboratory. This work, in collaboration with the Paris-Meudon Observatory, investigated how preprocessing techniques can improve the imaging of our Sun.

Jay is now currently a Tutor in Computer Science at Swansea University, where he is part of the Theoretical Computer Science research group. Jay is also part of the Swansea Team Researching Online Grooming (STRONG) and Swansea Icons Norm Group (SING) hosted in Psychology.

Personal website

Areas Of Expertise

Deep Learning
Natural Language Processing

Career Highlights

Teaching Interests

Statistics

Machine Learning

Programming Languages

Publications

Research Highlights

Morgan, J., Morgan, J., Paiement, A., Williams, J., Wyner, A., & Seisenberger, M. (2018). A Chatbot Framework for the Children’s Legal Centre. In Frontiers in Artificial Intelligence and Applications (pp. 205-209).
https://doi.org/10.3233/978-1-61499-935-5-205, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa48384
http://ebooks.iospress.nl/volumearticle/50853
Morgan, J. & MORGAN, J. (2022). Strategies to use Prior Knowledge to Improve the Performance of Deep Learning (Subtitle) An Approach Towards Trustable Machine Learning Systems
https://doi.org/10.23889/SUthesis.59258, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59258
Morgan, J., Paiement, A., & Klinke, C. (2023). Domain-informed graph neural networks: A quantum chemistry case study. Neural Networks, 165, 938-952.
https://doi.org/10.1016/j.neunet.2023.06.030, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63771
http://dx.doi.org/10.1016/j.neunet.2023.06.030
Morgan, J., Crook, T., Morgan, J., Pauly, A., & Roggenbach, M. (2023). A Computability Perspective on (Verified) Machine Learning. In Recent Trends in Algebraic Development Techniques (pp. 63-80). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-43345-0_3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63849

All Research

Journal Articles

Morgan, J., Paiement, A., & Klinke, C. (2023). Domain-informed graph neural networks: A quantum chemistry case study. Neural Networks, 165, 938-952.
https://doi.org/10.1016/j.neunet.2023.06.030, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63771
http://dx.doi.org/10.1016/j.neunet.2023.06.030

Conference Contributions

Morgan, J., Crook, T., Morgan, J., Pauly, A., & Roggenbach, M. (2023). A Computability Perspective on (Verified) Machine Learning. In Recent Trends in Algebraic Development Techniques (pp. 63-80). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-43345-0_3, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63849
Morgan, J., Morgan, J., Paiement, A., Williams, J., Wyner, A., & Seisenberger, M. (2018). A Chatbot Framework for the Children’s Legal Centre. In Frontiers in Artificial Intelligence and Applications (pp. 205-209).
https://doi.org/10.3233/978-1-61499-935-5-205, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa48384
http://ebooks.iospress.nl/volumearticle/50853

Other Research Outputs

Morgan, J. & MORGAN, J. (2022). Strategies to use Prior Knowledge to Improve the Performance of Deep Learning (Subtitle) An Approach Towards Trustable Machine Learning Systems
https://doi.org/10.23889/SUthesis.59258, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59258