SaTC: CORE: Medium: Robust Machine Learning at the Edge — NSF Award to Northeastern University (MA, $755,040)
Many safety-critical applications depend on the robustness of machine learning (ML) algorithms, i.e., their ability to make good predictions when exposed to previously unseen inputs. These safety-critical applications, such as autonomous vehicles, medical applications, wireless networks, and smart cities, often involve
| Award title | SaTC: CORE: Medium: Robust Machine Learning at the Edge |
|---|---|
| Award ID | 2414652 |
| Awardee | Northeastern University |
| City | BOSTON |
| State | MA |
| Amount obligated | $755,040 |
| Principal investigator | Stratis Ioannidis |
| Program | Secure &Trustworthy Cyberspace |
| Start date | 08/01/2025 |
| Abstract | Many safety-critical applications depend on the robustness of machine learning (ML) algorithms, i.e., their ability to make good predictions when exposed to previously unseen inputs. These safety-critical applications, such as autonomous vehicles, medical applications, wireless networks, and smart cities, often involve "edge devices" such as phones, sensors, and Internet-of-Things devices (e.g., wearable and smart home technology). These edge devices have computational, storage, and power limita |
| Source | NSF Awards |
$799/mo
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