Rate My Professor Damith Ranasinghe

Associate Professor Damith Ranasinghe serves in the School of Computer Science and Information Technology within the College of Engineering and Information Technology at the University of Adelaide. He obtained his PhD in Electrical and Electronic Engineering from the University of Adelaide in 2007. His academic career at the institution includes roles as Senior Lecturer from 2015 and promotion to Associate Professor from 2018. An engineer by inclination, Ranasinghe specializes in pervasive computing, machine learning, autonomous systems, artificial intelligence, and the security of embedded systems and AI. His research emphasizes multi-disciplinary approaches to tackle challenges in population ageing, wildlife conservation, product counterfeiting, and cloning. Key achievements encompass pioneering wearable sensor technology for falls prevention, facilitating the world's first trial in Australia that demonstrated reduced harm in geriatric evaluation and management units; designing affordable aerial robots including TrackerBots, ConservationBots, and GyroCopter for monitoring endangered species in rugged environments; and creating the Icicle emulator, the first multi-instruction-set-architecture tool for executing firmware from embedded devices like medical equipment, wearables, drones, and satellites on high-performance platforms for security analysis. His team has developed and open-sourced tools such as Multi-Fuzz, DyMA-Fuzz, FirmReBugger, and QUIC-Tester, enabling bug disclosures to vendors, fixes in open-source software, and studies on AI vulnerabilities including physical-world adversarial patches and Trojan attacks on object detectors.

Ranasinghe's contributions to literature include editing books like Unique Radio Innovation for the 21st Century: Building Scalable and Global RFID Networks (Springer-Verlag, 2010) and Networked RFID Systems and Lightweight Cryptography: Raising Barriers to Product Counterfeiting (Springer-Verlag, 2007). Prominent recent publications are 'Joint estimation of sea state and vessel parameters using a mass-spring-damper equivalence model' (Signal Processing, 2026), 'Distributed multi-object tracking under limited field of view heterogeneous sensors with density clustering' (Signal Processing, 2025), 'ConservationBots: Autonomous aerial robot for fast robust wildlife tracking in complex terrains' (Journal of Field Robotics, 2024), and 'Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects' (IEEE Transactions on Signal Processing, 2024). As Chief Investigator, he has obtained funding through ARC Linkage Grants such as $567,582 for Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion (2021-2024) and $272,000 for Airborne Spatial Tracking to Save Endangered Species (2016-2018), ARC Discovery Projects, and a $1,600,000 NHMRC Project Grant (2015-2017). He supervises Masters and PhD students, currently leading doctoral research in robot planning, distributed learning, and protocol testing.