Graphs are ubiquitous data structures providing powerful representations for objects with interactions. Empowered by recent progress in AI and machine learning, rapid technical progress has been achieved in graph mining. On the other hand, research and clinical practices in public health have generated large volumes of interconnected data, where the exploration of modern graph mining principles and techniques is still rather limited. In this talk, Dr. Yang will introduce their research vision and agenda for graph mining for health, followed by successful examples from their recent exploration of multi-modality graph construction, trustworthy graph modeling, and federated graph learning. Finally, Dr. Yang will conclude the talk with discussions on future directions that can benefit from further collaborations with researchers interested in data mining or health informatics in general.
Join us for an enlightening session! Let's explore graph data and delve into the latest techniques and their practical applications in healthcare.
Speaker(s): Carl Yang
Virtual: https://events.vtools.ieee.org/m/417510
