NSF award 2610630 · Harvard University

Collaborative Research: BEGIN with Data Bits: Leveraging Atomic Linearity for Multi-Resolution Statistical Inference and Machine Learning Interpretability

Final report due

28 October 2029

in 1,105 days · The award ends on 30 June 2029.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension2 Mar 2029in 865 days
award ending 2029-06-30 · Research.gov
This one is ours, not NSF’s.
Extension request16 May 2029in 940 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 975 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 1105 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 1105 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 1105 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

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.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2610630
Period of support: 1 Jul 2026 – 30 Jun 2029
Amount: $175,000
Awardee: Harvard University, Cambridge, MA
Program: STATISTICS
Directorate: MPS

Abstract

Reliable methods for learning from complex data, central to the field of Artificial Intelligence (AI), are essential for scientific discovery and for decisions that affect national health, prosperity, and welfare. Modern studies often collect measurements on many interacting variables, but standard statistical methods may require simplifying assumptions that are difficult to verify and may miss important…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.