Computational Science Group

About Us

The Computer Science Research Group in the School of Science and Technology at the University of New England brings together researchers with broad expertise spanning artificial intelligence, cybersecurity, data science, and communications theory. The group's work is characterised by a strong emphasis on real-world impact, with research applied to domains including agriculture and precision farming, healthcare and medical imaging, Industry 4.0 and smart manufacturing, wildlife monitoring, sport science, and critical infrastructure protection. Research strengths range from foundational contributions in algorithm design, coding theory, and computational modelling through to applied work in precision agriculture, medical AI, cybersecurity for cloud and IoT environments, and intelligent learning systems. This breadth reflects the group's commitment to both advancing computing science and delivering practical solutions to pressing challenges in regional, national, and global contexts.

Research Foci

Applied Computing; Computer Vision; Cybersecurity; Data Science; Human-Computer Interaction; Machine Learning; Networks and Communication

Team Members

• Professor William Billingsley
• Professor Ljiljana Brankovic
• Professor Raymond Chiong
• Associate Professor Subrata Chakraborty
• Associate Professor David Paul
• Associate Professor Uday Tupakula
• Associate Professor Mitchell Welch
• Dr Farshid Hajati
• Dr Fareed Ud-Din
• Dr Peter Loxley
• Dr Vera Miloslavskaya
• Dr Edmund Sadgrove
• Dr Andreas Shepley

Selected Publications

• Billingsley, W. (2025). The practical epistemologies of design and artificial intelligence. Science & Education, 34(2), 807-824.
• Sakzad, A., Paul, D., Sheard, J., Brankovic, L., Skerritt, M. P., Li, N., ... & Billingsley, W. (2024, March). Diverging assessments: what, why, and experiences. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 1161-1167).
• Alalawi, K., Athauda, R., & Chiong, R. (2025). An extended learning analytics framework integrating machine learning and pedagogical approaches for student performance prediction and intervention. International Journal of Artificial Intelligence in Education, 35(3), 1239-1287.
• Joshi, A., Pradhan, B., Chakraborty, S., Varatharajoo, R., Alamri, A., Gite, S., & Lee, C. W. (2025). An explainable Bi-LSTM model for winter wheat yield prediction. Frontiers in plant science, 15, 1491493.
• Alharbi, N., Din, F. U., Paul, D., & Sadgrove, E. (2025). Driving AI chatbot adoption: A systematic review of factors, barriers, and future research directions. Journal of Open Innovation: Technology, Market, and Complexity, 100590.
• Hariharan, S., Jerusha, Y. A., Suganeshwari, G., Ibrahim, S. S., Tupakula, U., & Varadharajan, V. (2025). A hybrid deep learning model for network intrusion detection system using seq2seq and convlstm-subnets. IEEE Access.
• Koudela, H., Schaerf, T. M., Lathlean, T., Murphy, A., & Welch, M. (2025). Investigating the emergence of collective states within rugby sevens gameplay. Journal of Sports Sciences, 43(1), 48-59.
• Zobeiri, A., Rezaee, A., Hajati, F., Argha, A., & Alinejad-Rokny, H. (2025). Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis. International Journal of Medical Informatics, 193, 105659.
• Hettikankanamage, N., Shafiabady, N., Chatteur, F., Wu, R. M., Din, F. U., & Zhou, J. (2025). eXplainable artificial intelligence (XAI): A systematic review for unveiling the black box models and their relevance to biomedical imaging and sensing. Sensors (Basel, Switzerland), 25(21), 6649.
• Loxley, P. N., & Cheung, K. W. (2023). A dynamic programming algorithm for finding an optimal sequence of informative measurements. Entropy, 25(2), 251.
• Miloslavskaya, V., Li, Y., & Vucetic, B. (2024). Frozen set design for precoded polar codes. IEEE Transactions on Communications, 73(1), 77-92.
• Ford, J., Sadgrove, E., & Paul, D. (2025). Joint plant-spraypoint detector with ConvNeXt modules and HistMatch normalization. Precision Agriculture, 26(1), 24.
• Shepley, A. J., Falzon, G., Kwan, P., & Brankovic, L. (2023). Confluence: A robust non-IoU alternative to non-maxima suppression in object detection. IEEE transactions on pattern analysis and machine intelligence, 45(10), 11561-11574.

Contact

Professor William Billingsley
Email: wbilling@une.edu.au
Phone: +61 2 6773 2513