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Stanford Graph Learning Workshop 2022

Event Details:

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

Location

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

Advances in Academia, Industry and the PyG Graph Learning Framework

Overview

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 - 09:00Registration & Breakfast 
09:00 - 09:30Welcome and Overview of Graph Representation Jure Leskovec, Stanford University
09:30 - 10:00What’s New in PyGMatthias Fey, PyG
10:00 - 10:20Building PyG Open Source Community  Ivaylo Bahtchevanov, PyG
10:20 - 10:40Scaling-up PyGManan Shah & Dong Wang, Kumo.ai
10:40 - 11:00Break 
11:00 - 11:20Accelerating PyG with Nvidia GPUsRishi Puri, Nvidia
11:20 - 11:40Accelerating PyG with Intel CPUsKe Ding, Intel
11:40 - 12:00Podcast Recommendations and Search Query Suggestions Using GNNs at Spotify Andreas Damianou, Spotify
12:00 - 12:20Enabling Enterprises to Query the Future using PyGHema Raghavan & Tin-Yun Ho, Kumo.ai
12:20 - 12:30The Stanford CS LINXS Summer Research ProgramJoseph Huang, Stanford University
12:30 - 13:30Lunch 
13:30 - 13:50Graph AI to Enable Precision Medicine Marinka Zitnik, Harvard University
13:50 - 14:10Challenges and Solutions in Applying Graph Neural Networks at GoogleBryan Peruzzi, Google
14:10 - 14:30Dynamic GNNs for Web Safety and IntegritySrijan Kumar, Georgia Institute of Technology
14:30 - 14:50Graph Mining for Next-Generation Intelligent Assistants on AR/VR DevicesLuna Dong, Meta
14:50 - 15:10Graph Learning in NLP ApplicationsMichi Yasunaga, Stanford University
15:10 - 15:30Break 
15:30 - 15:50Open Graph Benchmark: Large-Scale ChallengeWeihua Hu, Stanford University
15:50 - 16:10Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge GraphsHongyu Ren, Stanford University
16:10 - 17:00Industry 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
17:00Happy Hour 

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