About
Ben Mora is an Associate Professor in the department of Computer science, Swansea University.
Dr Benjamin Mora
Associate Professor
Computer Science
Office - 335
Third Floor
Computational Foundry
Bay Campus
Third Floor
Computational Foundry
Bay Campus
Available For Postgraduate Supervision
Publications
Research Highlights
-
Major, L., Clare, A., Daykin, J., Mora, B., & Zarges, C. (2025). Heuristics for the run-length encoded Burrows–Wheeler transform alphabet ordering problem. Journal of Heuristics, 31(1), 11
-
Duckworth, E., Hole, A., Deshmukh, A., Chaturvedi, P., Chilakapati, M., Mora, B., & Roy, D. (2022). Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer. Analytical Chemistry, 94(40), 13642-13646.
-
Eshkiki, H., Mora, B., & Xie, X. (2024). Fully Connected Networks on a Diet With the Mediterranean Matrix Multiplication. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 634-647.
-
Nagoor, O., Whittle, J., Deng, J., Mora, B., & Jones, M. (2020). Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 2815-2819). IEEE.
-
Nagoor, O., Whittle, J., Deng, J., Mora, B., Jones, M., & Whittle, J. (2021). MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM). In 2020 25th International Conference on Pattern Recognition (ICPR)IEEE.
All Research
Journal Articles
-
Major, L., Clare, A., Daykin, J., Mora, B., & Zarges, C. (2025). Heuristics for the run-length encoded Burrows–Wheeler transform alphabet ordering problem. Journal of Heuristics, 31(1), 11
-
Tang, Z., Duckworth, E., Mora, B., Sarireh, B., Mortimer, M., & Roy, D. (2024). Advancing frontline early pancreatic cancer detection using within-class feature extraction in FTIR spectroscopy. Scientific Reports, 14(1), 28940
-
Eshkiki, H., Mora, B., & Xie, X. (2024). Fully Connected Networks on a Diet With the Mediterranean Matrix Multiplication. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 634-647.
-
Duckworth, E., Hole, A., Deshmukh, A., Chaturvedi, P., Chilakapati, M., Mora, B., & Roy, D. (2022). Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer. Analytical Chemistry, 94(40), 13642-13646.
-
Nagoor, O., Whittle, J., Deng, J., Mora, B., & Jones, M. (2022). Sampling strategies for learning-based 3D medical image compression. Machine Learning with Applications, 8, 100273
-
Doidge, I., Jones, M., & Mora, B. (2012). Mixing Monte Carlo and progressive rendering for improved global illumination. The Visual Computer, 28(6-8), 603-612.
-
Clarke, L., Chen, M., Mora, B., & Mora, B. (2011). Automatic Generation of 3D Caricatures Based on Artistic Deformation Styles. IEEE Transactions on Visualization and Computer Graphics, 17(6), 808-821.
-
Mora, B. (2011). Naive ray-tracing. ACM Transactions on Graphics, 30(5), 1
-
Mora, B. (2011). Naive ray-tracing: A divide-and-conquer approach. ACM Transactions on Graphics, 30(5), 117
-
Mora, B., Maciejewski, R., Chen, M., Ebert, D., & Mora, B. (2009). Visualization and Computer Graphics on Isotropically Emissive Volumetric Displays. IEEE Transactions on Visualization and Computer Graphics, 15(2), 221
-
Aidong, L., Ebert, D., Qiao, W., Kraus, M., & Mora, B. (2007). Volume illustration using wang cubes. ACM Transactions on Graphics, 26(2), 11
-
Mora, B. & Ebert, D. (2005). Low-complexity maximum intensity projection. ACM Transactions on Graphics, 24(4), 1392
-
Mora, B. & Evert, D. (2004). Instant Volumetric Understanding with Order-Independent Volume Rendering. Computer Graphics Forum, 23(3), 489
Book Chapters
-
Tsitiridis, A., Mora, B., Richardson, M., & Mora, B. (2013). Hierarchical Object Recognition Model of Increased Invariance. Engineering Applications of Neural Networks -202).
Conference Contributions
-
Kapcia, M., Eshkiki, H., Duell, J., Fan, X., Zhou, S., & Mora, B. (2021). ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 841-845). IEEE.
-
Nagoor, O., Whittle, J., Deng, J., Mora, B., Jones, M., & Whittle, J. (2021). MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM). In 2020 25th International Conference on Pattern Recognition (ICPR)IEEE.
-
Nagoor, O., Whittle, J., Deng, J., Mora, B., & Jones, M. (2020). Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 2815-2819). IEEE.
-
Major, L., Clare, A., Daykin, J., Mora, B., Gamboa, L., & Zarges, C. (2020). Evaluation of a Permutation-Based Evolutionary Framework for Lyndon Factorizations. In Parallel Problem Solving from Nature – PPSN XVI (pp. 390-403). Springer International Publishing.
-
Ryan, A. & Mora, B. (2014). Variations on Image Reconstruction:Weighted Back Projection and Fourier Expectation Maximization. In Rita Borgo and Wen Tang (Ed.), Computer Graphics and Visual ComputingThe Eurographics Association.
-
Kammaje, R. & Mora, B. (2008). Row tracing using hierarchical occlusion maps. (pp. 27IEEE.
-
Barthe, L., Mora, B., Dodgson, N., Sabin, M., & Mora, B. (2002). Triquadratic reconstruction for interactive modelling of potential fields. (pp. 145
Supervision
-
Early Pancreatic Cancer Detection using Medical Imaging«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /» and Spectroscopy (current)
PhDOther supervisor: Dr Deb Roy -
New Algorithms For Machine Learning And Explainable Artificial Intelligence (awarded 2023)
PhDOther supervisor: Prof Xianghua Xie -
Untitled (current)
MScOther supervisor: Dr Hassan Eshkiki -
Original: Deep generative modelling for protein-ligand design: integrating Boltz-1 with reinforcement learning optimisation Amended: Integrating Boltz-1 with Direct Preference Optimisation for Protein–Ligand Design (current)
MScOther supervisor: Dr Yuanbo Wu
Taught Modules
-
CSCM37 Data Visualisation
Data Visualisation is concerned with the automatic or semi-automatic generation of digital images that depict data in a meaningful way(s). It is a relatively new field of computer science that is rapidly evolving and expanding. It is also very application oriented, i.e., real tools are built in order to help scientists from other disciplines.
-
CSCM437 Data Visualisation & Visual Analytics
.This module offers a comprehensive introduction to data abstraction and digital visualisation software, covering key concepts such as marks and channels, task abstraction, and the design process with a focus on colour. Students will explore visualisations for table, map, network, and tree data through practical lab sessions. The course also delves into volume visualisation techniques, including ray-casting, Marching Cubes, splatting, and texture-based rendering algorithms, as well as flow visualisation. The module concludes with an introduction to computer graphics, with lectures and labs designed to provide hands-on experience and feedback.
-
CSCM598 Operating Systems and Architectures
This module provides a comprehensive overview of processes, threads, and concurrency, beginning with an introduction to programs, processes, and threads, followed by practical lab classes on multithreading and concurrency. It covers resource management, CPU architectures, low-level programming, and memory management, with hands-on labs on ring buffers and SIMD programming. The module also delves into shell programming, graphics card architecture, FPGA architectures, inter-process communications, and file systems, culminating in a study of distributed systems.