NSF award 2543755 · University of Chicago

CAREER: Active Representation Learning for Real-World Adaptive Experimental Design

Final report due

28 November 2031

in 1,867 days · The award ends on 31 July 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 extension2 Apr 2031in 1627 days
award ending 2031-07-31 · Research.gov
This one is ours, not NSF’s.
Extension request16 Jun 2031in 1702 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Jul 2031in 1737 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Nov 2031in 1867 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Nov 2031in 1867 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Nov 2031in 1867 days
award ending 2031-07-31 · 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 July 2031.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 November 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: 2543755
Period of support: 1 Aug 2026 – 31 Jul 2031
Amount: $569,125
Awardee: University of Chicago, Chicago, IL
Program: Robust Intelligence
Directorate: CSE

Abstract

Artificial intelligence is increasingly used to guide scientific discovery, engineering design, and complex decision-making, where each experiment or trial can be costly and time-consuming. A central challenge is how to efficiently identify the most informative experiments from vast and complex design spaces, especially when observations are limited and uncertainty is high. This project develops a new paradigm for…

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.