ERI: Flexible, Adaptive, and Explainable Anomaly Detection under Distribution Shift in Mul — NSF Award to Louisiana Tech Universit
This Engineering Research Initiation (ERI) grant will fund research that seeks to develop adaptive and explainable monitoring methods supporting real-time operational decision-making under uncertainty. Modern engineering systems, including manufacturing, energy infrastructure, and aerospace platforms, rely on continuou
| Award title | ERI: Flexible, Adaptive, and Explainable Anomaly Detection under Distribution Shift in Mul |
|---|---|
| Award ID | 2552579 |
| Awardee | Louisiana Tech University |
| City | RUSTON |
| State | LA |
| Amount obligated | $185,210 |
| Principal investigator | Abdur Rahman |
| Program | OE Operations Engineering |
| Start date | 06/01/2026 |
| Abstract | This Engineering Research Initiation (ERI) grant will fund research that seeks to develop adaptive and explainable monitoring methods supporting real-time operational decision-making under uncertainty. Modern engineering systems, including manufacturing, energy infrastructure, and aerospace platforms, rely on continuous monitoring to operate safely and efficiently. Although these systems generate large volumes of data, identifying abnormal behavior remains difficult because operating conditions |
| Source | NSF Awards |
$799/mo
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