Collaborative Research: Online Statistical Inference for Modern Machine Learning — NSF Award to Washington University (MO, $99,322
This research aims to develop statistical tools to improve the reliability of artificial intelligence (AI) that is widely used in real-world systems such as automated decision-making, financial forecasting, and neuroscience research. Modern AI often relies on efficient machine learning algorithms to process large-scale
| Award title | Collaborative Research: Online Statistical Inference for Modern Machine Learning |
|---|---|
| Award ID | 2515927 |
| Awardee | Washington University |
| City | SAINT LOUIS |
| State | MO |
| Amount obligated | $99,322 |
| Principal investigator | Likai Chen |
| Program | STATISTICS |
| Start date | 10/01/2025 |
| Abstract | This research aims to develop statistical tools to improve the reliability of artificial intelligence (AI) that is widely used in real-world systems such as automated decision-making, financial forecasting, and neuroscience research. Modern AI often relies on efficient machine learning algorithms to process large-scale, sequentially arriving datasets. While these algorithms are powerful, understanding their behavior and measuring their uncertainty remains a major scientific challenge. To bridge |
| Source | NSF Awards |
$799/mo
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