Rate My Professor Brendan McCane

Professor Brendan McCane is a Professor in the School of Computing at the University of Otago, where he also serves as Associate Dean (Academic) for the Division of Sciences. In this role, he supports and monitors academic work across the division's departments, focusing on curriculum development, quality assurance, and the implementation of university policies. He leads strategic developments in academic matters at both divisional and university levels. McCane's academic background is in Computer Science. He completed his undergraduate studies and PhD at James Cook University of North Queensland in Australia. His PhD, entitled "Learning to Recognise 3D Objects from 2D Intensity Images," was awarded in February 1996. Following a temporary position as a lecturer at James Cook University, he joined the Department of Computer Science at the University of Otago in February 1997 as a lecturer. He has remained at Otago, advancing to his current position as Professor.

McCane's research interests include computer vision, pattern recognition, machine learning, biomedical imaging, robotics, and computer graphics. His current research focuses on theoretical understanding of the effectiveness of deep networks and self-learning for robots. He participates in the Otago AI Group, with contributions to reinforcement learning, support vector machines, convolutional neural networks, deep learning, catastrophic forgetting, and models of visual perception. Key publications include "Recovering Motion Fields: An Evaluation of Eight Optical Flow Algorithms" (1998), "On Benchmarking Optical Flow" (2001), "SIFT and SURF Performance Evaluation Against Various Image Deformations on Benchmark Dataset" (2011), "LOOP Descriptor: Local Optimal-Oriented Pattern" (2018), "Pseudo-Rehearsal: Achieving Deep Reinforcement Learning Without Catastrophic Forgetting" (2021), "Cross-modal Image Generation with Uncertainty Quantification from Echocardiogram to MRI" (2026, Methods), "Conditional Autoencoder for Generating Binary Neutron Star Waveforms with Tidal and Precession Effects" (2025, Physical Review D), and "Identification of Stochastic Gravitational Wave Backgrounds from Cosmic Strings Using Machine Learning" (2025, Physical Review D). He is a member of the University of Otago Scholarships and Prizes Committee.