CAREER: Exploring Non-Euclidean Representation Learning for Expressive and Explainable Graph-based Foundation Models

This project addresses a critical limitation in current artificial intelligence systems: their inability to accurately understand and represent the complex network-like relationships that exist in the real world. Existing state-of-the-art models lack flexibility when modeling such data in areas like biological networks, drug discovery, or…