CAREER: Active Representation Learning for Real-World Adaptive Experimental Design — NSF Award to University of Chicago (IL, $342,
Artificial intelligence is increasingly used to guide scientific discovery, engineering design, and complex decision-making, where each experiment or trial can be costly and time-consuming. A central challenge is how to efficiently identify the most informative experiments from vast and complex design spaces, especiall
| Award title | CAREER: Active Representation Learning for Real-World Adaptive Experimental Design |
|---|---|
| Award ID | 2543755 |
| Awardee | University of Chicago |
| City | CHICAGO |
| State | IL |
| Amount obligated | $342,196 |
| Principal investigator | Yuxin Chen |
| Program | Robust Intelligence |
| Start date | 08/01/2026 |
| Abstract | Artificial intelligence is increasingly used to guide scientific discovery, engineering design, and complex decision-making, where each experiment or trial can be costly and time-consuming. A central challenge is how to efficiently identify the most informative experiments from vast and complex design spaces, especially when observations are limited and uncertainty is high. This project develops a new paradigm for adaptive experimental design that enables learning systems to not only model data |
| Source | NSF Awards |
$799/mo
Try NSFGrants →