CAREER: Fast and Certifiable Nonconvex Optimization for Perception and Control of Autonomo — NSF Award to Harvard University (MA,
This NSF CAREER project aims to make autonomous systems safer and more reliable by developing optimization tools that are both fast and theoretically sound. Many high-stakes autonomy tasks—building 3D maps from images, planning robot motions, or choosing actions from raw sensor data—are formulated as nonconvex optimiza
| Award title | CAREER: Fast and Certifiable Nonconvex Optimization for Perception and Control of Autonomo |
|---|---|
| Award ID | 2543352 |
| Awardee | Harvard University |
| City | CAMBRIDGE |
| State | MA |
| Amount obligated | $545,000 |
| Principal investigator | Heng Yang |
| Program | EPCL: Energy, Power, Control, |
| Start date | 06/01/2026 |
| Abstract | This NSF CAREER project aims to make autonomous systems safer and more reliable by developing optimization tools that are both fast and theoretically sound. Many high-stakes autonomy tasks—building 3D maps from images, planning robot motions, or choosing actions from raw sensor data—are formulated as nonconvex optimization problems that today are typically handled by heuristics that can fail unpredictably. This project will bring transformative change by enabling “certifiable” decision-making: a |
| Source | NSF Awards |
$799/mo
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