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Creates dynamic and thought-provoking lessons.
Always positive and motivating in class.
A true mentor who cares about success.
Always goes the extra mile for students.
Makes even hard topics easy to grasp.
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About Mo
Mohammed Hossny, known professionally as Mo Hossny, is a Senior Lecturer in the School of Systems & Computing at UNSW Canberra, University of New South Wales. He earned his Bachelor’s degree in computer science from Cairo University, followed by a Master’s degree in computer science developed in collaboration with the IBM Centre of Advanced Studies. Hossny completed his PhD at the Institute for Intelligent Systems Research and Innovation at Deakin University, where he created an algebraic framework for multimodal image fusion. In his current role at UNSW, his research centers on human performance, approached through markerless motion capture techniques and biomechanics. This work intersects artificial intelligence, computer vision, and human-centered computing, with applications in areas such as posture stabilization, intent prediction for vulnerable road users, and ocular biomechanics.
Hossny has authored or co-authored 55 publications, including 27 journal articles, 25 conference papers, and 3 book chapters. Notable works include 'VoxelScape: Large Scale Simulated 3D Point Cloud Dataset of Urban Traffic Environments' (IEEE Transactions on Intelligent Transportation Systems, 2023), 'An ocular biomechanics environment for reinforcement learning' (Journal of Biomechanics, 2022), and 'Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users' (IEEE Transactions on Intelligent Transportation Systems, 2020). Earlier contributions feature 'A skeleton-free fall detection system from depth images using random decision forest' (IEEE Systems Journal, 2018) and foundational work on image fusion such as 'Image fusion performance metric based on mutual information and entropy driven quadtree decomposition' (Electronics Letters, 2010). His research has garnered over 4,100 citations, achieving an h-index of 32 and an i10-index of 77 according to Google Scholar. Affiliated with the School of Engineering and Information Technology, Hossny contributes to advancements in AI-driven motion analysis and human-machine interaction at UNSW.
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