CAREER: PTM-SEER: Software Engineering Foundations for Re-Using Pre-Trained Neural Models — NSF Award to Purdue University (IN, $4
Modern computing systems increasingly incorporate learned components using techniques from machine learning and artificial intelligence. Engineering practice favors reuse over building from scratch. However, while for conventional software we know much about the re-use and adaptation of components, the correspondence f
| Award title | CAREER: PTM-SEER: Software Engineering Foundations for Re-Using Pre-Trained Neural Models |
|---|---|
| Award ID | 2541917 |
| Awardee | Purdue University |
| City | WEST LAFAYETTE |
| State | IN |
| Amount obligated | $401,546 |
| Principal investigator | James Davis |
| Program | Software & Hardware Foundation |
| Start date | 06/01/2026 |
| Abstract | Modern computing systems increasingly incorporate learned components using techniques from machine learning and artificial intelligence. Engineering practice favors reuse over building from scratch. However, while for conventional software we know much about the re-use and adaptation of components, the correspondence for pre-trained models is an emerging and evolving concern. Engineers must decide which models to trust, how to adapt them, and how to document their behavior, often without shared |
| Source | NSF Awards |
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