Carl Yang of Emory University

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.

Biography:

Dr. Carl Yang is an Assistant Professor of Computer Science at Emory University. He received his Ph.D. in Computer Science at University of Illinois, Urbana-Champaign in 2020, and B.Eng. in Computer Science and Engineering at Zhejiang University in 2014. His research interests span graph data mining, applied machine learning, knowledge graphs and federated learning, with applications in recommender systems, social networks, neuroscience and healthcare. Carl's research results have been published in 120+ peer-reviewed papers in top venues across data mining and health informatics. He is also a recipient of the Dissertation Completion Fellowship of UIUC in 2020, the Best Paper Award of ICDM in 2020, the Best Paper Award of KDD Health Day in 2022, the Best Paper Award of ML4H in 2022, the Amazon Research Award in 2022, the Microsoft Accelerating Foundation Models Research Award in 2023, and multiple Emory internal research awards. Carl's research receives funding support from both NSF and NIH of USA.

Address:United States