CAREER: Statistically Grounded Generative AI: Uncertainty Quantification and Principled De — NSF Award to Columbia University (NY,
Generative artificial intelligence can create realistic text, images, and other complex data, offering new ways to support science, education, industry, and public decision-making. However, these systems can also make errors that are difficult for users to detect or measure. As a result, organizations may not know when
| Award title | CAREER: Statistically Grounded Generative AI: Uncertainty Quantification and Principled De |
|---|---|
| Award ID | 2544147 |
| Awardee | Columbia University |
| City | NEW YORK |
| State | NY |
| Amount obligated | $243,000 |
| Principal investigator | Kaizheng Wang |
| Program | STATISTICS |
| Start date | 09/01/2026 |
| Abstract | Generative artificial intelligence can create realistic text, images, and other complex data, offering new ways to support science, education, industry, and public decision-making. However, these systems can also make errors that are difficult for users to detect or measure. As a result, organizations may not know when generated data can be trusted, whether a system works equally well across different populations, or how its performance changes over time. This project addresses these challenges |
| Source | NSF Awards |
$799/mo
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