Random Matrix Theory and Manifold Learning for High-Dimensional Data Integration — NSF Award to Harvard University (MA, $175,000)
This project develops new mathematical and computational tools for integrating high-dimensional datasets with partially shared structures, a challenge that arises across various fields, including molecular biology, precision medicine, business analytics, and economics. When data are collected from multiple sources—such
| Award title | Random Matrix Theory and Manifold Learning for High-Dimensional Data Integration |
|---|---|
| Award ID | 2515684 |
| Awardee | Harvard University |
| City | CAMBRIDGE |
| State | MA |
| Amount obligated | $175,000 |
| Principal investigator | Rong Ma |
| Program | STATISTICS |
| Start date | 07/01/2026 |
| Abstract | This project develops new mathematical and computational tools for integrating high-dimensional datasets with partially shared structures, a challenge that arises across various fields, including molecular biology, precision medicine, business analytics, and economics. When data are collected from multiple sources—such as different individuals, experimental conditions, or technologies—joint analysis can reveal complex patterns that would be missed if each dataset were analyzed in isolation. Howe |
| Source | NSF Awards |
$799/mo
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