CICI: IPAAI: Multi-Layer Data Provenance and Federated Learning for Securing Scientific AI — NSF Award to University of Virginia M
Artificial intelligence (AI) is becoming essential to scientific discovery in areas, such as biomedical research, environmental modeling, and genomics. However, the reliability of AI systems depends on the quality and integrity of the data used to train them. Scientific datasets are often collected from multiple source
| Award title | CICI: IPAAI: Multi-Layer Data Provenance and Federated Learning for Securing Scientific AI |
|---|---|
| Award ID | 2530655 |
| Awardee | University of Virginia Main Campus |
| City | CHARLOTTESVILLE |
| State | VA |
| Amount obligated | $900,000 |
| Principal investigator | Wajih Ul Hassan |
| Program | Cybersecurity Innovation |
| Start date | 01/01/2026 |
| Abstract | Artificial intelligence (AI) is becoming essential to scientific discovery in areas, such as biomedical research, environmental modeling, and genomics. However, the reliability of AI systems depends on the quality and integrity of the data used to train them. Scientific datasets are often collected from multiple sources, including laboratory instruments, simulations, and collaborative institutions. This variability makes it difficult to verify how data were generated, processed, or applied. This |
| Source | NSF Awards |
$799/mo
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