CAREER: Learning and Selecting Low-Dimensional Models from Incomplete Data — NSF Award to University of Wisconsin-Madison (WI, $47
Big datasets often have an underlying structure. Identifying such a structure allows predicting outcomes of interest based on a few variables, for example, predicting the effectiveness of a drug or vaccine based on the drug’s molecular structure. There exists a wide variety of methods to learn the underlying structure
| Award title | CAREER: Learning and Selecting Low-Dimensional Models from Incomplete Data |
|---|---|
| Award ID | 2239479 |
| Awardee | University of Wisconsin-Madison |
| City | MADISON |
| State | WI |
| Amount obligated | $478,608 |
| Principal investigator | Daniel Pimentel-Alarcon |
| Program | Comm & Information Foundations |
| Start date | 02/01/2023 |
| Abstract | Big datasets often have an underlying structure. Identifying such a structure allows predicting outcomes of interest based on a few variables, for example, predicting the effectiveness of a drug or vaccine based on the drug’s molecular structure. There exists a wide variety of methods to learn the underlying structure of a dataset and make accurate predictions. However, when data is severely incomplete, as is the case in many modern datasets, existing methods consistently fail to identify the co |
| Source | NSF Awards |
$799/mo
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