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About Brendon
Dr. Brendon Woodford is a Senior Lecturer in the School of Computing, Sciences Division, at the University of Otago. He earned his PhD from the University of Otago in 2003. Dr. Woodford lectures in knowledge engineering, machine learning, information systems development, and health informatics. His research specializations encompass machine learning, pattern recognition, fuzzy systems, and image processing, as indicated on his Google Scholar profile, where his work has garnered 726 citations. He is associated with the Pattern Recognition and Machine Learning research group and the Data Sciences Research Group at the University of Otago. Additionally, he contributes to the Centre for Artificial Intelligence and Public Policy.
Dr. Woodford's publication record includes contributions to journals and conferences. Notable publications are 'Illumination Invariant Background Model Using Mixture of Gaussians' (2013, Computer Vision - ACCV 2012 Workshops, co-authored with M. Shah and J.D. Deng); 'An assessment of existing wildfire danger indices in comparison to one-class machine learning models' (2024, Natural Hazards, with F.N. Ismail and S.A. Licorish); 'Understanding Regression Models on Stack Overflow Code' (2025, Journal of Software, Vol. 20(2), pp. 51-65, with L.K. Vithanage and O.P. Omondiagbe); 'Using machine learning approaches to classify false positives in static code analysis' (2025, Zenodo); and 'Taking a Closer Look at Warnings Generated by PMD and SpotBugs' (2025, Zenodo). He has supervised PhD theses including 'Novel machine learning approaches for wildfire prediction to aid decision making' by F.N. Ismail (2022), 'Crowd Scene Analysis in Video Surveillance' by H. Lin (2016), and 'Adaptive foreground-background segmentation of complex video sequences' by S.M.H. Shah (2014). Dr. Woodford serves on program committees for PAKDD 2025 and PAKDD 2024, and participated in the university panel 'A Ruthless Critique of AI' in 2024. His work addresses applications in wildfire prediction, software code analysis, video surveillance, and AI ethics.
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