Dr Brano Kusy

Dr. Brano Kusy is a Principal Research Scientist at CSIRO, where he leads the Distributed Sensing Systems research group. His research involves networked embedded systems, mobile and wearable computing, and the Internet of Things. He focuses on developing sensing technology for real-time monitoring, automation, and interactivity of physical world environments. He has designed and deployed energy-efficient sensing systems to support industrial productivity and safety, as well as to monitor natural ecosystems at scale. Dr. Kusy's work emphasizes the real-world deployment and validation of research, particularly in applying edge AI models in dynamic and challenging environments.

Dr. Kusy's technical expertise includes scalable, energy-efficient distributed sensing systems, with a focus on algorithms for coordinated control, synchronization, localization, wireless communications, on-device machine learning, and adaptive sampling. He served as the General Chair for IPSN’20, PC Co-Chair of IPSN’24 and participates in technical program committees for top-rank international conferences such as ACM SenSys, ACM/IEEE IPSN, EWSN, and IEEE ICDCS. His publications have received over 8500 citations with an h-index of 38, and his work on the Flooding Time-Synchronization Protocol was recognized with the Test of Time Award from the ACM SenSys conference. Dr. Kusy holds a Master’s degree in computer science from Comenius University, Slovakia, and a PhD in computer science from Vanderbilt University, USA. Before joining CSIRO, he was a postdoctoral researcher at Stanford University with Professor Leo Guibas.

Key research projects:

• Sensor/AI Systems for Wildlife and Environments. Design and implementation of multi-modal sensor systems for monitoring motion traces, behaviour, and physiology of animal and plant species, linked to ecosystem rehabilitation, reduced impact of feral species and revegetation of industrial sites. Vision-based surveillance methods for real-time assessment of underwater habitats. On-device real-time AI algorithms to classify behaviour, feeding patterns of animals. Large-scale computer vision ML platforms for monitoring species populations.

• Mine ecosystems. Wireless sensor network for microclimate sensing and analytics for surface mine rehabilitation. Automation of post-mining land rehabilitation. Underground communications for sensor communication and actuation in mining.

• Efficient and Robust Low-power Radio Communication. Design and evaluation of interference-robust multi-radio platforms with improved delivery rates, network stability, and transmission costs at minimal or no increase in energy cost over mainstream radios and radio protocols.

• Building Energy Efficiency. Application of pervasive computing to improve energy usage in office appliances and heating, cooling, and ventilation systems