Non-parametric estimation for multimodal data: From statistical theory to efficient algori — NSF Award to University of California
Multimodal datasets, which combine sources such as medical imaging, clinical records, and genetic information, have the potential to significantly advance our understanding of complex systems and improve health outcomes. However, the heterogeneity, high dimensionality, and lack of reliable statistical tools often lead
| Award title | Non-parametric estimation for multimodal data: From statistical theory to efficient algori |
|---|---|
| Award ID | 2515903 |
| Awardee | University of California-Los Angeles |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $139,995 |
| Principal investigator | Xiaowu Dai |
| Program | STATISTICS |
| Start date | 06/01/2026 |
| Abstract | Multimodal datasets, which combine sources such as medical imaging, clinical records, and genetic information, have the potential to significantly advance our understanding of complex systems and improve health outcomes. However, the heterogeneity, high dimensionality, and lack of reliable statistical tools often lead to unstable analyses or misleading conclusions. These issues — and the limited ability to rigorously quantify uncertainty or disentangle relationships among data sources — pose a m |
| Source | NSF Awards |
$799/mo
Try NSFGrants →