Learning and Inference on Graph-structured Data
Wei Ye, the University of California, Santa Barbara
2020-05-13 14:00:00 ~ 2020-05-13 15:00:00
Graph-structured data is ubiquitous in the real-world. Because graph is a natural way to represent the relationships between objects, it has attracted a lot of attention. However, graph-structured data have no spatial or temporal order inside. Standard deep learning architectures such as CNNs and RNNs cannot directly work on them.
Wei Ye is currently a postdoctoral researcher with the DYNAMO lab at the University of California, Santa Barbara. Before joining the DYNAMO lab, he worked as a researcher in the Department of AI Platform, Tencent, China. He obtained his PhD degree in Computer Science from Institute for Informatics, Ludwig-Maximilians University of Munich, Germany, in 2018. His research interests include graph machine learning and their applications, causal inference and reasoning, and dynamic networks.