This seminar introduces an integrated AI framework for real-time environmental monitoring, combining Machine Learning, Symbolic AI, Causal Inference, and Digital Twin technology. By leveraging satellite data and advanced remote sensing technologies, alongside knowledge graphs, we create predictive, explainable, and ethically responsible systems for disaster detection and environmental management.

The speaker will also introduce several research and industrial projects conducted with governments and industries in the space and mining sectors, showcasing real-world applications such as bushfire monitoring, flood damage estimation, water reservoir volume measurement, methane emission tracking, and crocodile monitoring.

Associate Professor Jun Jo earned his PhD from the University of Sydney in 1994, where his research focused on Artificial Intelligence, particularly in symbolic reasoning and knowledge-based systems. As part of his PhD, he developed an autonomous design system called EDGE. Following his doctorate, Dr. Jo served as a Postdoctoral Research Fellow at the Key Centre of Design Computing at the University of Sydney before joining Griffith University in 1996.

Dr. Jo has led numerous research projects across diverse fields, applying data analysis with satellite, IoT, and drone data to areas such as bushfire detection, medical image analysis, robotics, UAVs, and eLearning. He has published over 150 refereed papers, and his research team explores cutting-edge AI technologies, including advanced machine learning models such as transformers and LLMs, as well as symbolic reasoning techniques like knowledge graphs. Dr. Jo has organised the International Robot Olympiad in Australia twice, in 2006 and 2010. Currently, he serves as the Chair of the International Robot Olympiad Committee (IROC) and the President of the Australian Robotics Association (ARA).