Excellence in Research: Mitigating Confounding Errors in Real-World Machine Learning for R — NSF Award to Alabama State University
User-generated contents, such as online reviews or social media posts, often contain hidden information like education, personal preferences, location, or language. This information, known as confounding factors, can affect the contents and impact the outcomes of decision systems. When applying machine learning for rea
| Award title | Excellence in Research: Mitigating Confounding Errors in Real-World Machine Learning for R |
|---|---|
| Award ID | 2502941 |
| Awardee | Alabama State University |
| City | MONTGOMERY |
| State | AL |
| Amount obligated | $674,160 |
| Principal investigator | Qiunan Zhang |
| Program | HBCU-EiR - HBCU-Excellence in, HCC-Human-Centered Computing |
| Start date | 10/01/2025 |
| Abstract | User-generated contents, such as online reviews or social media posts, often contain hidden information like education, personal preferences, location, or language. This information, known as confounding factors, can affect the contents and impact the outcomes of decision systems. When applying machine learning for real-world decision support, those confounding factors can easily have negative effects on model generalizability and usability. This project focuses on identifying and mitigating con |
| Source | NSF Awards |
$799/mo
Try NSFGrants →