Title: Graph Neural Networks: A Feature and Structure Learning Approach
Abstract: In the real world, many data are naturally represented as graph data such as social networks. Deep learning methods have been very successful in various fields such as computer vision and natural language processing. However, developing deep learning methods on graph data is challenging due to the lack of locality information. My work addresses this challenge and significantly advances feature learning and structure learning on graphs in both accuracy and efficiency. Specifically, I proposed several deep learning methods on graph data such as learnable convolution, graph hard attention, and graph pooling operations. This series of research works result in a series of publications in top-tier journals and conferences.
Bio: Hongyang Gao received his Ph.D. degree from Texas A&M University, College Station, Texas, in 2020. Currently, he is an Assistant Professor in the Department of Computer Science, Iowa State University, Ames, Iowa. His research interests include machine learning, deep learning, and data mining. Before his Ph.D. work, he received his M.S. from Tsinghua University in 2012 and his B.S. from Peking University in 2009.
After the presentation, there will be a short time for discussion and questions afterwards.