Collaborative Research: BEGIN with Data Bits: Leveraging Atomic Linearity for Multi-Resolu — NSF Award to Harvard University (MA,
Reliable methods for learning from complex data, central to the field of Artificial Intelligence (AI), are essential for scientific discovery and for decisions that affect national health, prosperity, and welfare. Modern studies often collect measurements on many interacting variables, but standard statistical methods
| Award title | Collaborative Research: BEGIN with Data Bits: Leveraging Atomic Linearity for Multi-Resolu |
|---|---|
| Award ID | 2610630 |
| Awardee | Harvard University |
| City | CAMBRIDGE |
| State | MA |
| Amount obligated | $175,000 |
| Principal investigator | Xiao-Li Meng |
| Program | STATISTICS |
| Start date | 07/01/2026 |
| Abstract | Reliable methods for learning from complex data, central to the field of Artificial Intelligence (AI), are essential for scientific discovery and for decisions that affect national health, prosperity, and welfare. Modern studies often collect measurements on many interacting variables, but standard statistical methods may require simplifying assumptions that are difficult to verify and may miss important relationships in the data. This project will develop a new way to understand such relationsh |
| Source | NSF Awards |
$799/mo
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