Red X iconGreen tick iconYellow tick icon
Brendon Woodford imageNZCDP(Stage III), BSc, PGCertTertT, PGDipSci, MSc, PhD (Otago)
Senior Lecturer

Room 3.47, Otago Business School
Tel +64 3 479 5432

Background and interests

Dr Brendon Woodford lectures in the diverse areas of knowledge engineering, machine learning, information systems development, and health informatics.

He conducts research for the Knowledge, Intelligence and Web Informatics (KIWI) Laboratory in the areas of artificial neural networks, fuzzy systems, data visualisation, and image processing and recognition. Since 1998 the overall aim of this research is in the area of computational intelligence. The main objective of his work has been to create adaptive learning systems which generate new knowledge from data that they process and allow for improved decision making in difficult real-world domains.

In the past the application of this work has been improving upon existing machine learning techniques to support decision making and data mining primarily in the horticultural domain but the current focus is now in the health data analytics domain.

This research has contributed to 27 publications to date in both international journals and in some of the top international conferences. In 2008 he completed a PhD which primarily looked at the development and implementation of intelligent decision support systems for New Zealand's horticulture industry.

Dr Woodford is also a member of the research group:
Intelligent Computing and Networking


  • COMP 111 Information and Communications Technology
  • COMP 120 Practical Data Science
  • INFO 204 Introduction to Data Science
  • INFO 411 Machine Learning and Data Mining


Currently supervising

  • Nuzla Ismail

Currently co-supervising

  • Ahmad Shahi


Ghandour, A., Woodford, B. J., & Abusaimeh, H. (2024). Ethical considerations in the use of ChatGPT: An exploration through the lens of five moral dimensions. IEEE Access, 12, 60682-60693. doi: 10.1109/ACCESS.2024.3394243 Journal - Research Article

Ismail, F. N., Sengupta, A., Woodford, B. J., & Licorish, S. A. (2024). A comparison of one-class versus two-class machine learning models for wildfire prediction in California. In D. Benavides-Prado, S. Erfani, P. Fournier-Viger, Y. L. Boo & Y. S. Koh (Eds.), Data Science and Machine Learning: Proceedings of the 21st Australasian Conference, AusDM 2023 [Communications in Computer and Information Science 1943]. (pp. 239-253). Singapore: Springer. doi: 10.1007/978-981-99-8696-5_17 Conference Contribution - Published proceedings: Full paper

Woodford, B. J. (2023). Automatic parameter optimisation framework for ECoS-based models. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN54540.2023.10191789 Conference Contribution - Published proceedings: Full paper

Ghandour, A., Woodford, B. J., Almutairy, A., & Al-Srehan, H. S. (2022). Means to support the ends: Forums that escape the official gaze in the educational institutions. Proceedings of the International Arab Conference on Information Technology (ACIT). IEEE. doi: 10.1109/ACIT57182.2022.9994133 Conference Contribution - Published proceedings: Full paper

Woodford, B. J. (2022). Boosted self-evolving neural networks for pattern recognition. Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 13728). (pp. 456-469). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22695-3_32 Conference Contribution - Published proceedings: Full paper

Back to top