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November 16 | Virtual & Free

SigOpt AI & HPC Summit 2021


Panel: Optimizing and Scaling Graph Neural Networks

9 - 9:45am

In the past five years, graph neural networks (GNNs) have emerged as a very powerful family of neural network architectures that operate directly on graph data structures. It is very powerful that GNNs model tasks at the level of a dataset’s (often) more natural graph representation, but the algorithms that enable this generate some side effects when it comes to scaling GNNs to large graphs. They are very memory- and compute-hungry models so going beyond benchmark datasets to real-world biology, physics, e-commerce and social network problems is challenging and will require researching and developing new techniques. Join us for a conversation with industry leading researchers from AWS, PayPal, and Intel Labs to discuss how they think about optimizing and scaling graph neural networks and what data scientists should know if they want to work with these models.

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Add to Calendar 11/16/2021 9:00 am 11/16/2021 9:45 am America/Los_Angeles Panel: Optimizing and Scaling Graph Neural Networks SigOpt AI & HPC Summit 2021 - Virtual & Free


Sasikanth (Sasi) Avancha

Senior Research Scientist, Intel Parallel Compute Labs

Eddie Mattia

Eddie Mattia

Product Manager, SigOpt

Venkatesh Ramanathan

Director Data Science, PayPal

Da Zheng

Senior Applied Scientist, AWS AI