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:

• Computer vision and deep learning

• Surveillance analytics and behaviour understanding

• Multi-object detection and tracking

• Reinforcement learning and autonomous robotics

• Intelligent real-time monitoring systems

• Thermal and low-light computer vision

• Drone detection and tracking

• 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.