CAREER: Beyond Multi-Index Models: Statistical and Algorithmic Foundations for Feature Lea — NSF Award to Northwestern University
Across science and engineering, from biology, econometrics to aerodynamics, data are often high-dimensional and complex, yet the outcomes of interest are frequently governed by only a few critical factors interacting in structured ways. Despite major advances, a significant gap remains between interpretable statistical
| Award title | CAREER: Beyond Multi-Index Models: Statistical and Algorithmic Foundations for Feature Lea |
|---|---|
| Award ID | 2540678 |
| Awardee | Northwestern University at Chicago |
| City | EVANSTON |
| State | IL |
| Amount obligated | $244,168 |
| Principal investigator | Feng Ruan |
| Program | STATISTICS |
| Start date | 06/01/2026 |
| Abstract | Across science and engineering, from biology, econometrics to aerodynamics, data are often high-dimensional and complex, yet the outcomes of interest are frequently governed by only a few critical factors interacting in structured ways. Despite major advances, a significant gap remains between interpretable statistical methods and modern high-performing predictive systems. Classical statistical approaches, such as multi-index models, seek to capture these mechanisms through compositions of linea |
| Source | NSF Awards |
$799/mo
Try NSFGrants →