Trusted selective and predictive inference tools for modern data-driven applications — NSF Award to University of Pennsylvania (PA
Recent years have witnessed numerous exciting opportunities brought by the abundance of data and powerful machine learning algorithms. Along with the opportunities, it has been recognized that the complex nature of modern data and models makes them hard to analyze: the data can be high-dimensional and correlated in com
| Award title | Trusted selective and predictive inference tools for modern data-driven applications |
|---|---|
| Award ID | 2413135 |
| Awardee | University of Pennsylvania |
| City | PHILADELPHIA |
| State | PA |
| Amount obligated | $119,587 |
| Principal investigator | Zhimei Ren |
| Program | STATISTICS |
| Start date | 07/01/2024 |
| Abstract | Recent years have witnessed numerous exciting opportunities brought by the abundance of data and powerful machine learning algorithms. Along with the opportunities, it has been recognized that the complex nature of modern data and models makes them hard to analyze: the data can be high-dimensional and correlated in complicated ways, while the models are often of unprecedented sizes and black-box to the users. There is, therefore, a need for new statistical methodologies for understanding such da |
| Source | NSF Awards |
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