NSF award 2601682 · William Marsh Rice University

Collaborative Research: MFAI: Conformality-Guided Learning with Evolving and Heterogeneous Data

Final report due

28 November 2029

in 1,137 days · The award ends on 31 July 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 Apr 2029in 897 days
award ending 2029-07-31 · Research.gov
This one is ours, not NSF’s.
Extension request16 Jun 2029in 972 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Jul 2029in 1007 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Nov 2029in 1137 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Nov 2029in 1137 days
award ending 2029-07-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Nov 2029in 1137 days
award ending 2029-07-31 · 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 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 November 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: 2601682
Period of support: 1 Aug 2026 – 31 Jul 2029
Amount: $250,000
Awardee: William Marsh Rice University, Houston, TX
Program: MSPA-INTERDISCIPLINARY, Special Projects - CCF
Directorate: MPS

Abstract

Reliable uncertainty estimates are essential for using artificial intelligence systems in high-stakes settings, including health care and electric grid operations. This project develops new methods that help learning systems quantify prediction uncertainty and communicate it through prediction sets that are both valid and useful. The work addresses a central obstacle in modern artificial intelligence: data used to…

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.