Dr Sankaran Iyer
PhD: Computer Science, UNSW 2023
MCompSc: UNSW 1994
BE (Hons): Electrical and Electronics Engineering from Birla Institute of Technology and Science Pilani (India)
Dr Sankaran Iyer obtained his PhD from UNSW Sydney in 2023, where his research focused on vertebral compression fracture detection using a novel 3D localisation framework that combined deep reinforcement learning and imitation learning. His work explored supervised, weakly supervised, and semi-supervised learning approaches for medical image analysis and localisation.
He also completed a Master’s degree in Computer Science at UNSW in 1994, with research focused on Latin character detection using artificial neural networks.
Dr Iyer has over 30 years of industry experience spanning real-time embedded systems, intelligent networks, and operations support systems. Prior to returning to academia, he worked at Nokia/Alcatel-Lucent, where he voluntarily retired in 2016 as a Senior Project Manager.
He has collaborated with researchers from the Biological, Earth and Environmental Sciences (BEES) group at UNSW on projects involving house dust mite and pest detection systems, as well as an Android-based wildlife species detection application developed as part of the Bushfire Recovery program.
Currently, Dr Iyer is a Senior Research Associate at UNSW working in collaboration with the Black Dog Institute on AI-based suicide detection and prevention research. His work focuses on behaviour analysis in public environments such as railway stations, bridges, parks, and shopping centres using pedestrian detection, tracking, pose estimation, and anomaly detection techniques.
His broader research interests include:
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Computer vision and deep learning
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Surveillance analytics and behaviour understanding
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Multi-object detection and tracking
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Reinforcement learning and autonomous robotics
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Intelligent real-time monitoring systems
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Thermal and low-light computer vision
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Drone detection and tracking
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AI for safety-critical environments
Dr Iyer is also exploring learning-based autonomous indoor robotics, including intelligent inspection, continual reinforcement learning, and vision-guided navigation using robotic platforms and simulation environments.
- Publications
- Media
- Grants
- Awards
- Research Activities
- Engagement
- Teaching and Supervision
My research activities focus on Deep Learning and Computer Vision, particularly in the areas of:
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Object detection and multi-object tracking
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Behaviour analysis and anomaly detection
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Surveillance analytics and intelligent monitoring systems
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Reinforcement learning and autonomous robotics
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AI for safety-critical and defence-related applications
Currently, I work as a Senior Research Associate at UNSW in collaboration with the Black Dog Institute, focusing on AI-driven behaviour analysis for suicide detection and prevention in public environments such as railway stations, bridges, parks, and shopping centres. This work involves pedestrian detection and tracking, pose estimation, spatiotemporal behaviour analysis, and anomaly detection using deep learning techniques.
My broader research interests include intelligent surveillance and autonomous systems operating in complex real-world environments, including thermal and low-light conditions, long-range monitoring, and small-object detection.
Current and ongoing project areas include:
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Anomaly detection and tracking in public surveillance systems
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Crowd behaviour analysis and intent recognition
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Drone detection and tracking in restricted airspace
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Smart parking and intelligent transport monitoring systems
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Retail analytics using computer vision technologies
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Intruder detection in restricted and defence-related environments
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Vision-guided autonomous indoor robotics and intelligent inspection systems
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Continual reinforcement learning for adaptive robotic navigation
My work combines research and practical system development, with a strong emphasis on robust real-time AI solutions for operational environments.
My Research Supervision
I am currently co-supervising 2 PhD students and guiding a Master of Information Science student. Additionally, I assist other students with coding and model building in PyTorch, TensorFlow, and other deep learning platforms.