CAREER: Defending Machine Learning Models from Adversarial Threats via Unified Interpretab — NSF Award to Rochester Institute of T
Machine learning models increasingly power critical systems in healthcare, finance, and national security. However, their increasing complexity introduces serious risks, as attackers can embed hidden vulnerabilities or exploit obscure failure modes. The project’s novelties are bridging the gap between powerful modern s
| Award title | CAREER: Defending Machine Learning Models from Adversarial Threats via Unified Interpretab |
|---|---|
| Award ID | 2543795 |
| Awardee | Rochester Institute of Tech |
| City | ROCHESTER |
| State | NY |
| Amount obligated | $344,911 |
| Principal investigator | Weijie Zhao |
| Program | Secure &Trustworthy Cyberspace |
| Start date | 04/01/2026 |
| Abstract | Machine learning models increasingly power critical systems in healthcare, finance, and national security. However, their increasing complexity introduces serious risks, as attackers can embed hidden vulnerabilities or exploit obscure failure modes. The project’s novelties are bridging the gap between powerful modern systems and classical, understandable frameworks to build machine learning models that are both secure and interpretable. Instead of treating simpler models as mere baselines, the r |
| Source | NSF Awards |
$799/mo
Try NSFGrants →