Tensor decomposition for multi-context data analysis — NSF Award to Harvard University (MA, $400,000)
Across scientific domains, a fundamental challenge is to understand how systems change across contexts. Examples include biological processes across diseases or word meanings across genres of text. Tensors are a natural framework to study such multi-context systems, generalizing the role of matrices and linear algebra
| Award title | Tensor decomposition for multi-context data analysis |
|---|---|
| Award ID | 2608217 |
| Awardee | Harvard University |
| City | CAMBRIDGE |
| State | MA |
| Amount obligated | $400,000 |
| Principal investigator | Anna Seigal |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 06/01/2026 |
| Abstract | Across scientific domains, a fundamental challenge is to understand how systems change across contexts. Examples include biological processes across diseases or word meanings across genres of text. Tensors are a natural framework to study such multi-context systems, generalizing the role of matrices and linear algebra in classical data analysis methods such as principal component analysis. The investigator will design new tensor decomposition algorithms and use them to analyze multi-context data |
| Source | NSF Awards |
$799/mo
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