Learning from Hidden Signatures in High-Dimensional Models — NSF Award to Cornell University (NY, $300,000)
Methods directly applicable to the determination of regulatory cell mechanisms associated with vaccine responses will be developed as an integral part of this project. Such mechanisms are not directly observable or measurable, but a wealth of indirect measurements can be obtained, for instance on antibody titer-driven
| Award title | Learning from Hidden Signatures in High-Dimensional Models |
|---|---|
| Award ID | 2015195 |
| Awardee | Cornell University |
| City | ITHACA |
| State | NY |
| Amount obligated | $300,000 |
| Principal investigator | Florentina Bunea |
| Program | STATISTICS |
| Start date | 07/01/2020 |
| Abstract | Methods directly applicable to the determination of regulatory cell mechanisms associated with vaccine responses will be developed as an integral part of this project. Such mechanisms are not directly observable or measurable, but a wealth of indirect measurements can be obtained, for instance on antibody titer-driven effects. New methodology, that meets the challenge of extracting these hidden signals from a very large volume of data, consisting of a diverse arrays of indirect measurements, are |
| Source | NSF Awards |
$799/mo
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