NSF award 2613279 · Tufts University

Learnable Tensor Algebras for Harnessing Implicit Correlations in Multiway Data

Final report due

29 December 2027

in 436 days · The award ends on 31 August 2027.

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Decide about an extension3 May 2027in 196 days
award ending 2027-08-31 · Research.gov
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Extension request17 Jul 2027in 271 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2027in 306 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2027in 436 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2027in 436 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2027in 436 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 August 2027.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2027.

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Award card

Award number: 2613279
Period of support: 15 Jan 2026 – 31 Aug 2027
Amount: $230,000
Awardee: Tufts University, Medford, MA
Program: COMPUTATIONAL MATHEMATICS
Directorate: MPS

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,…

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