NSF award 2624954 · University of Pittsburgh

EAGER: Trustworthy and Verifiable Machine Unlearning in an Adversarial Environment

Final report due

28 January 2029

in 833 days · The award ends on 30 September 2028.

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 Jun 2028in 593 days
award ending 2028-09-30 · Research.gov
This one is ours, not NSF’s.
Extension request16 Aug 2028in 668 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Sep 2028in 703 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Jan 2029in 833 days
award ending 2028-09-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Jan 2029in 833 days
award ending 2028-09-30 · 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 September 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 January 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: 2624954
Period of support: 1 Oct 2026 – 30 Sep 2028
Amount: $299,572
Awardee: University of Pittsburgh, Pittsburgh, PA
Program: Secure &Trustworthy Cyberspace
Directorate: CSE

Abstract

AI models are increasingly being trained on a huge amount of data. These models continuously need to remove selected data, or correct them to support a better accuracy or utility, and/or to comply with regulatory requirements such as the right to be forgotten/erasure/rectification. Machine Unlearning (MU) has emerged as a critical approach to support efficient removal of data, including privacy-sensitive or…

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.