CAREER: AdaptTrust: Adaptable, Trustworthy and Uncertainty-Aware Learning in Intelligent S — NSF Award to Rochester Institute of T
This NSF CAREER project aims to develop machine learning systems that continuously learn from new data without forgetting prior knowledge. Today’s machine learning models are powerful but largely static, often overwriting earlier information when updated, a problem known as catastrophic forgetting that reduces reliabil
| Award title | CAREER: AdaptTrust: Adaptable, Trustworthy and Uncertainty-Aware Learning in Intelligent S |
|---|---|
| Award ID | 2542166 |
| Awardee | Rochester Institute of Tech |
| City | ROCHESTER |
| State | NY |
| Amount obligated | $560,000 |
| Principal investigator | Dimah Dera |
| Program | EPCL: Energy, Power, Control, |
| Start date | 04/01/2026 |
| Abstract | This NSF CAREER project aims to develop machine learning systems that continuously learn from new data without forgetting prior knowledge. Today’s machine learning models are powerful but largely static, often overwriting earlier information when updated, a problem known as catastrophic forgetting that reduces reliability in evolving environments, such as healthcare monitoring, environmental sensing, and autonomous systems. The project will bring transformative change by enabling intelligent sys |
| Source | NSF Awards |
$799/mo
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