NSF award 2608264 · University of California-Los Angeles

Mathematical Foundations and Scalable Algorithms for High-Dimensional Sampling and Generative Modeling

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: 2608264
Period of support: 15 Aug 2026 – 31 Jul 2029
Amount: $300,000
Awardee: University of California-Los Angeles, LOS Angeles, CA
Program: OFFICE OF MULTIDISCIPLINARY AC, COMPUTATIONAL MATHEMATICS
Directorate: MPS

Abstract

Artificial intelligence and scientific computing increasingly rely on mathematical models of uncertainty. A central task is to generate representative possible outcomes from these models, a process known as sampling. In scientific inference, the model combines scientific knowledge with observations, while in generative artificial intelligence, it is learned from data. Modern problems may involve enormous numbers of…

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.