NSF award 2638918 · Rutgers University Newark

CAREER: Modern Aspects of Learning-Augmented Algorithms

Final report due

28 August 2031

in 1,775 days · The award ends on 30 April 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 extension31 Dec 2030in 1535 days
award ending 2031-04-30 · Research.gov
This one is ours, not NSF’s.
Extension request16 Mar 2031in 1610 days
award ending 2031-04-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Apr 2031in 1645 days
award ending 2031-04-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Aug 2031in 1775 days
award ending 2031-04-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Aug 2031in 1775 days
award ending 2031-04-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Aug 2031in 1775 days
award ending 2031-04-30 · 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 April 2031.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 August 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: 2638918
Period of support: 1 Oct 2026 – 30 Apr 2031
Amount: $711,498
Awardee: Rutgers University Newark, Newark, NJ
Program: Algorithmic Foundations
Directorate: CSE

Abstract

Many important artificial intelligence computing systems rely on algorithms to make decisions about scheduling, routing, and the use of limited resources. Traditional algorithms are dependable because their performance can be guaranteed in all cases, but they often cannot take advantage of recurring patterns in data that could make them faster or more effective. By contrast, machine learning methods can detect…

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.