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UID:20250713T000158EDT-4842wA0u3L@132.216.98.100
DTSTAMP:20250713T040158Z
DESCRIPTION:Title: Graph Representation Learning and Applications.\n\nAbstr
 act:\n	Graphs\, a general type of data structures for capturing interconnec
 ted objects\, are ubiquitous in a variety of disciplines and domains rangi
 ng from computational social science\, recommender systems\, medicine\, bi
 oinformatics to chemistry. Representative examples of real-world graphs in
 clude social networks\, user-item networks\, protein-protein interaction n
 etworks\, and molecular structures\, which are represented as graphs. In t
 his 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.\, mo
 lecule structures).\n\n\n	Speaker:\n	Dr. Jian Tang is an assistant professor
  at HEC Montreal and also a core faculty member at Mila since December\, 2
 017. He is named to the first cohort of Canada CIFAR Artificial Intelligen
 ce Chairs (CIFAR AI Research Chair). His research interests focus on deep 
 graph representation learning with a variety of applications including nat
 ural language understanding\, knowledge graphs\, drug discovery and recomm
 ender systems. He was a research fellow in University of Michigan and Carn
 egie Mellon University. He received his Ph.D degree from Peking University
  and was a visiting student in University of Michigan for two years. He wa
 s a researcher in Microsoft Research Asia for two years. He received the b
 est paper award of ICML’14 and nominated for the best paper of WWW’16.\n
DTSTART:20190426T193000Z
DTEND:20190426T203000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Jian Tang (HEC Montreal)
URL:/mathstat/channels/event/jian-tang-hec-montreal-29
 6488
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