NSF award 2543795 · Rochester Institute of Tech

CAREER: Defending Machine Learning Models from Adversarial Threats via Unified Interpretability and Attribution

Final report due

29 July 2031

in 1,745 days · The award ends on 31 March 2031.

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 extension1 Dec 2030in 1505 days
award ending 2031-03-31 · Research.gov
This one is ours, not NSF’s.
Extension request14 Feb 2031in 1580 days
award ending 2031-03-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Mar 2031in 1615 days
award ending 2031-03-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Jul 2031in 1745 days
award ending 2031-03-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Jul 2031in 1745 days
award ending 2031-03-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Jul 2031in 1745 days
award ending 2031-03-31 · 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 31 March 2031.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 July 2031.

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: 2543795
Period of support: 1 Apr 2026 – 31 Mar 2031
Amount: $549,519
Awardee: Rochester Institute of Tech, Rochester, NY
Program: Secure &Trustworthy Cyberspace
Directorate: CSE

Abstract

Machine learning models increasingly power critical systems in healthcare, finance, and national security. However, their increasing complexity introduces serious risks, as attackers can embed hidden vulnerabilities or exploit obscure failure modes. The project’s novelties are bridging the gap between powerful modern systems and classical, understandable frameworks to build machine learning models that are both…

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.