Fwd: LTI Colloquium: Jure Leskovec, 4/5/19, How Powerful Are Graph Neural Networks?

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Emily Ahn

Apr 2, 2019, 10:12:59 AM4/2/19
to pitt...@googlegroups.com, Han, Na-Rae
Hello everyone,

We have Jure Leskovec from Stanford, giving our Colloquium talk this Friday!

---------- Forwarded message ---------
From: Tessa Samuelson <tes...@andrew.cmu.edu>
Date: Tue, Apr 2, 2019 at 10:10 AM
Subject: LTI Colloquium: Jure Leskovec, 4/5/19, How Powerful Are Graph Neural Networks?
To: <lti-s...@cs.cmu.edu>

Hello everyone,

Have you ever wondered how powerful graph neural networks are? Interested in learning methods that automatically learn to encode graph structure into low-dimensional embeddings? If so, this colloquium talk is the one for you.

Jure Leskovec is our LTI colloquium guest speaker for this week, 4/05/19.

Where: Doherty Hall 2315  
When: 2:30-3:50 pm
When: Friday, April 5th, 2019



       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.



       Jure Leskovec <http://cs.stanford.edu/~jure> 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, their evolution, and the diffusion of information over them.

      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, the Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University.


Videos for LTI Colloquium can be found here

(the video for Heng Ji is not uploaded yet, she requested for her video to be made visible for only the CMU community, so I was told this would take a few more days to provide a link. Thanks for your patience!)

Attached is the list of upcoming LTI speakers.

Hope to see everyone there to learn about the power of graph neural networks!

Tessa G. Samuelson

Language Technologies Institute
Carnegie Mellon University
6719 Gates Hillman Center
LTI Colloquium Speakers Spring 2019.pdf
Jure Leskovec, Poster.pdf
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