CAREER: Exploring Non-Euclidean Representation Learning for Expressive and Explainable Gra — NSF Award to Yale University (CT, $59
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
| Award title | CAREER: Exploring Non-Euclidean Representation Learning for Expressive and Explainable Gra |
|---|---|
| Award ID | 2540656 |
| Awardee | Yale University |
| City | NEW HAVEN |
| State | CT |
| Amount obligated | $596,523 |
| Principal investigator | Rex Ying |
| Program | Info Integration & Informatics |
| Start date | 06/01/2026 |
| Abstract | 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 scientific literature retrieval systems, fundamentally due to the disparity between the complex structure of the data, and the Euclidean geometry properties of |
| Source | NSF Awards |
$799/mo
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