Collaborative Research: Novel Bayesian Thresholding and Shrinkage Methods in Multiscale Do — NSF Award to Texas A&M University (TX
Modern science and engineering increasingly rely on extracting meaningful information from large and noisy datasets, such as those arising in medical imaging, environmental monitoring, telecommunications, and numerous other disciplines. This project develops advanced statistical methods that improve signal recovery and
| Award title | Collaborative Research: Novel Bayesian Thresholding and Shrinkage Methods in Multiscale Do |
|---|---|
| Award ID | 2515246 |
| Awardee | Texas A&M University |
| City | COLLEGE STATION |
| State | TX |
| Amount obligated | $100,123 |
| Principal investigator | Brani Vidakovic |
| Program | STATISTICS |
| Start date | 09/01/2025 |
| Abstract | Modern science and engineering increasingly rely on extracting meaningful information from large and noisy datasets, such as those arising in medical imaging, environmental monitoring, telecommunications, and numerous other disciplines. This project develops advanced statistical methods that improve signal recovery and noise reduction through innovative shrinkage and thresholding techniques applied in multiscale domains like wavelets. In addition to classical computational tools, the project exp |
| Source | NSF Awards |
$799/mo
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