Learn how to build and analyze heterogeneous graphs using PyG, a machine graph learning library in Python. This workshop will provide a practical introduction to the concept of heterogeneous graphs and their applications, including their ability to capture the complexity and diversity of real-world systems. Participants will gain experience in creating a heterogeneous graph from multiple data tables, preparing a dataset, and implementing and training a model using PyG.
talk-data.com
M
Speaker
Matthias Fey
1
talks
Founding Engineer
Kumo.AI
Matthias Fey is the creator of PyTorch Geometric (PyG), a leading library for representation learning on graphs. At Kumo.ai, he heads the Research team, driving innovation in foundation models, graph based architectures and scalable ML systems. Prior to this, he completed his PhD on Message Passing for Learning over Graph Structured Data at TU Dortmund University. His work bridges early-stage research with real-world outcomes across a wide range of industry applications.
Bio from: AI-Powered Data & Search: Unlocking Intelligence Across Systems
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