Adaptive Inference by Stabilized Cross-Validation — NSF Award to Carnegie Mellon University (PA, $250,000)
Modern data analysis and statistical learning are characterized by two defining features: complex data structures and black-box algorithms. The complexity of data structures arises from advanced data collection technologies and data-sharing infrastructures, such as imaging, remote sensing, wearable devices, and genomic
| Award title | Adaptive Inference by Stabilized Cross-Validation |
|---|---|
| Award ID | 2515687 |
| Awardee | Carnegie Mellon University |
| City | PITTSBURGH |
| State | PA |
| Amount obligated | $250,000 |
| Principal investigator | Jing Lei |
| Program | STATISTICS |
| Start date | 09/01/2025 |
| Abstract | Modern data analysis and statistical learning are characterized by two defining features: complex data structures and black-box algorithms. The complexity of data structures arises from advanced data collection technologies and data-sharing infrastructures, such as imaging, remote sensing, wearable devices, and genomic sequencing. In parallel, black-box algorithms—particularly those stemming from advances in deep neural networks—have demonstrated remarkable success on modern datasets. This confl |
| Source | NSF Awards |
$799/mo
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