MIDAS Seminar Series Presents: Jure Leskovec – Stanford University

Jure Leskovec

Associate Professor, Stanford University
Chief Scientist of Pinterest
Investigator at Chan Zuckerberg Biohub

How Powerful are Graph Neural Networks?

Abstract: Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including graph convolutional networks and their representational power. We will also discuss applications to web-scale recommender systems, healthcare, and knowledge representation and reasoning.

Bio: Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining large social, information, and biological networks. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, marketing, and biomedicine. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, Alfred P. Sloan Fellowship, and numerous best paper and test of time awards. Leskovec received his bachelor’s degree in computer science from the University of Ljubljana, Slovenia, and his PhD in machine learning from Carnegie Mellon University and postdoctoral training at Cornell University.

For more information on MIDAS or the Seminar Series, please contact midas-contact@umich.edu. MIDAS gratefully acknowledges Wacker Chemie AG for its generous support of the MIDAS Seminar Series.