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Title: Graph Representation Learning and Applications.

Abstract:
Graphs, a general type of data structures for capturing interconnected objects, are ubiquitous in a variety of disciplines and domains ranging from computational social science, recommender systems, medicine, bioinformatics to chemistry. Representative examples of real-world graphs include social networks, user-item networks, protein-protein interaction networks, and molecular structures, which are represented as graphs. In this talk, I will introduce our work on learning effective representations of graphs such as learning low-dimensional node representations of large graphs (e.g., social networks, protein-protein interaction graphs, and knowledge graphs) and learning representations of entire graphs (e.g., molecule structures).

Speaker:
Dr. Jian Tang is an assistant professor at HEC Montreal and also a core faculty member at Mila since December, 2017. He is named to the first cohort of Canada CIFAR Artificial Intelligence Chairs (CIFAR AI Research Chair). His research interests focus on deep graph representation learning with a variety of applications including natural language understanding, knowledge graphs, drug discovery and recommender systems. He was a research fellow in University of Michigan and Carnegie Mellon University. He received his Ph.D degree from Peking University and was a visiting student in University of Michigan for two years. He was a researcher in Microsoft Research Asia for two years. He received the best paper award of ICML’14 and nominated for the best paper of WWW’16.