NSF award 2515684 · Harvard University

Random Matrix Theory and Manifold Learning for High-Dimensional Data Integration

Final report due

28 October 2029

in 1,106 days · The award ends on 30 June 2029.

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Decide about an extension2 Mar 2029in 866 days
award ending 2029-06-30 · Research.gov
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Extension request16 May 2029in 941 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Jun 2029in 976 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Oct 2029in 1106 days
award ending 2029-06-30 · 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 30 June 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 October 2029.

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Award card

Award number: 2515684
Period of support: 1 Jul 2026 – 30 Jun 2029
Amount: $175,000
Awardee: Harvard University, Cambridge, MA
Program: STATISTICS
Directorate: MPS

Abstract

This project develops new mathematical and computational tools for integrating high-dimensional datasets with partially shared structures, a challenge that arises across various fields, including molecular biology, precision medicine, business analytics, and economics. When data are collected from multiple sources—such as different individuals, experimental conditions, or technologies—joint analysis can reveal…

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