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TILOS Seminar: Towards Foundation Models for Graph Reasoning and AI 4 Science
Michael Galkin, Research Scientist at AI Lab
HDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002
Abstract: Foundation models in graph learning are hard to design due to the lack of common invariances that transfer across different structures and domains. In this talk, I will give an overview of the two main tracks of my research at Intel AI: creating foundation models for knowledge graph reasoning that can run zero-shot inference on any multi-relational graphs, and foundation models for materials discovery in the AI4Science domain that capture physical properties of crystal structures and transfer to a variety of predictive and generative tasks. We will also talk about theoretical and practical challenges like scaling behavior, data scarcity, and diverse evaluation of foundation graph models.
Bio: Michael Galkin is a Research Scientist at Intel AI Lab in San Diego working on Graph Machine Learning and Geometric Deep Learning. Previously, he was a postdoc at Mila – Quebec AI Institute with Will Hamilton, Reihaneh Rabbany, and Jian Tang, focusing on many graph representation learning problems. Sometimes, Mike writes long blog posts on Medium about graph learning.