Unsupervised Learning and Nonlinear Dimension Reduction: Advances with Optimal Transport, — NSF Award to Columbia University (NY,
Modern scientific data sets—ranging from single-cell RNA sequencing with tens of thousands of genes per patient, to galaxy-survey spectra with millions of stars, to user-item interaction matrices in online platforms—share two features: (i) ultra-high dimensionality and (ii) latent parameters that obey common structural
| Award title | Unsupervised Learning and Nonlinear Dimension Reduction: Advances with Optimal Transport, |
|---|---|
| Award ID | 2515520 |
| Awardee | Columbia University |
| City | NEW YORK |
| State | NY |
| Amount obligated | $240,000 |
| Principal investigator | Bodhisattva Sen |
| Program | STATISTICS |
| Start date | 07/01/2025 |
| Abstract | Modern scientific data sets—ranging from single-cell RNA sequencing with tens of thousands of genes per patient, to galaxy-survey spectra with millions of stars, to user-item interaction matrices in online platforms—share two features: (i) ultra-high dimensionality and (ii) latent parameters that obey common structural laws (e.g., exchangeability, sparsity, or low-rank dependence). This project tackles both challenges at once. It advances statistical foundations for such problems by (1) providin |
| Source | NSF Awards |
$799/mo
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