Event Details:
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 - 09:00 | 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, Kumo.ai |
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, Kumo.ai |
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 Peruzzi, 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 |
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17:00 | Happy Hour |