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Stanford Graph Learning Workshop 2022

Event Details:

Wednesday, September 28, 2022
8:00am - 5:00pm PDT


Paul Brest Hall
555 Salvatierra Walk
Stanford, CA 94305
United States

Advances in Academia, Industry and the PyG Graph Learning Framework



Over the past few years, graphs have emerged as one of the most important and useful abstractions for representing complex data, including social networks, knowledge graphs, financial transactions / purchasing behavior, supply chain networks, molecular graphs, biomedical networks, as well as for modeling 3D objects, manifolds, and source code. Deep representation learning on graphs is an emerging field with a wide array of applications, ranging from protein folding and fraud detection, to drug discovery and recommender systems.

In the Stanford Graph Learning Workshop, we will bring together thought leaders from academia and industry to showcase the most cutting edge and recent methodological advances in Graph Neural Networks. The workshop will present new developments in the leading graph machine learning framework and a wide range of graph machine learning applications to different domains. Additionally, the workshop will discuss practical challenges for large-scale training and deployment of graph-based machine learning models.



08:00 - 09:00

Registration & Breakfast

09:00 - 09:30 Welcome and Overview of Graph Representation  Jure Leskovec, Stanford University
09:30 - 10:00 What’s New in PyG Matthias Fey, PyG
10:00 - 10:20 Building PyG Open Source Community   Ivaylo Bahtchevanov, PyG
10:20 - 10:40 Scaling-up PyG Manan Shah & Dong Wang,
10:40 - 11:00 Break  
11:00 - 11:20 Accelerating PyG with Nvidia GPUs Rishi Puri, Nvidia
11:20 - 11:40 Accelerating PyG with Intel CPUs Ke Ding, Intel
11:40 - 12:00 Podcast Recommendations and Search Query Suggestions Using GNNs at Spotify  Andreas Damianou, Spotify
12:00 - 12:20 Enabling Enterprises to Query the Future using PyG Hema Raghavan & Tin-Yun Ho,
12:20 - 12:30 The Stanford CS LINXS Summer Research Program Joseph Huang, Stanford University
12:30 - 13:30 Lunch  
13:30 - 13:50 Graph AI to Enable Precision Medicine  Marinka Zitnik, Harvard University
13:50 - 14:10 Challenges and Solutions in Applying Graph Neural Networks at Google Bryan Perozzi, Google
14:10 - 14:30 Dynamic GNNs for Web Safety and Integrity Srijan Kumar, Georgia Institute of Technology
14:30 - 14:50 Graph Mining for Next-Generation Intelligent Assistants on AR/VR Devices Luna Dong, Meta
14:50 - 15:10 Graph Learning in NLP Applications Michi Yasunaga, Stanford University
15:10 - 15:30 Break  
15:30 - 15:50 Open Graph Benchmark: Large-Scale Challenge Weihua Hu, Stanford University
15:50 - 16:10 Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs Hongyu Ren, Stanford University
16:10 - 17:00 Industry Panel - Challenges and Opportunities for Graph Learning
  • Naren Chittar, JPMorgan Chase (moderator)
  • Evan Feinberg, Genesis Therapeutics
  • Yunyao Li, Apple
  • Neil Shah, Snap
  • Karthik Subbian, Amazon
17:00 Happy Hour  



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