Learnable Tensor Algebras for Harnessing Implicit Correlations in Multiway Data — NSF Award to Tufts University (MA, $77,082)
Big data has revolutionized the kinds of problems we can tackle, enabling unprecedented personalization and innovation across commercial, scientific, and healthcare applications. The ever-growing amount of data has created a pressing need for new methodologies to reduce storage demands and extract representative featur
| Award title | Learnable Tensor Algebras for Harnessing Implicit Correlations in Multiway Data |
|---|---|
| Award ID | 2613279 |
| Awardee | Tufts University |
| City | MEDFORD |
| State | MA |
| Amount obligated | $77,082 |
| Principal investigator | Elizabeth Newman |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 01/15/2026 |
| Abstract | Big data has revolutionized the kinds of problems we can tackle, enabling unprecedented personalization and innovation across commercial, scientific, and healthcare applications. The ever-growing amount of data has created a pressing need for new methodologies to reduce storage demands and extract representative features for downstream analysis. Many data, such as those arising in computer vision and imaging, neuroscience, networks (e.g., epidemic tracking, cyber security), and more, are nativel |
| Source | NSF Awards |
$799/mo
Try NSFGrants →