CPS: Medium: Latent Representation Learning for Verifiable Sensor Rich Systems — NSF Award to University of Southern California (C
Recent advances in artificial intelligence and machine learning offer a unique opportunity to develop the next generation of autonomous systems for high-impact applications such as search and rescue missions, natural disaster prevention, and personalized robotics. However, because AI systems inevitably exhibit some deg
| Award title | CPS: Medium: Latent Representation Learning for Verifiable Sensor Rich Systems |
|---|---|
| Award ID | 2434460 |
| Awardee | University of Southern California |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $1,200,000 |
| Principal investigator | Stephen Tu |
| Program | CPS-Cyber-Physical Systems |
| Start date | 08/01/2025 |
| Abstract | Recent advances in artificial intelligence and machine learning offer a unique opportunity to develop the next generation of autonomous systems for high-impact applications such as search and rescue missions, natural disaster prevention, and personalized robotics. However, because AI systems inevitably exhibit some degree of error, a major obstacle to their widespread deployment is ensuring they operate safely and reliably in real-world environments—while minimizing the risk of catastrophic fail |
| Source | NSF Awards |
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