Collaborative Research: Causal Learning with High-dimensional Imaging Outcomes: Methods, T — NSF Award to University of Virginia M
The analysis of imaging outcomes is a dynamic and rapidly evolving research field, driven by the growing accessibility of large-scale biomedical imaging databases. Imaging data, often characterized as functional data, presents unique opportunities and challenges for statistical analysis. Existing methods, however, are
| Award title | Collaborative Research: Causal Learning with High-dimensional Imaging Outcomes: Methods, T |
|---|---|
| Award ID | 2515788 |
| Awardee | University of Virginia Main Campus |
| City | CHARLOTTESVILLE |
| State | VA |
| Amount obligated | $124,487 |
| Principal investigator | Shan Yu |
| Program | STATISTICS |
| Start date | 09/01/2025 |
| Abstract | The analysis of imaging outcomes is a dynamic and rapidly evolving research field, driven by the growing accessibility of large-scale biomedical imaging databases. Imaging data, often characterized as functional data, presents unique opportunities and challenges for statistical analysis. Existing methods, however, are insufficient for handling the computational demands of large-scale medical imaging data or addressing issues such as unmeasured confounding and population heterogeneity in causal a |
| Source | NSF Awards |
$799/mo
Try NSFGrants →