NSF award 2551615 · Cornell University

Collaborative Research: III: Graph-based Vector Databases, from Theory to Practice

Final report due

28 October 2030

in 1,471 days · The award ends on 30 June 2030.

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 Mar 2030in 1231 days
award ending 2030-06-30 · Research.gov
This one is ours, not NSF’s.
Extension request16 May 2030in 1306 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice20 Jun 2030in 1341 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Oct 2030in 1471 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Oct 2030in 1471 days
award ending 2030-06-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Oct 2030in 1471 days
award ending 2030-06-30 · 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 June 2030.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 October 2030.

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: 2551615
Period of support: 15 Jul 2026 – 30 Jun 2030
Amount: $200,000
Awardee: Cornell University, Ithaca, NY
Program: Info Integration & Informatics
Directorate: CSE

Abstract

In the last several years, machine learning and artificial intelligence have transformed how computers catalog and search for documents, images, videos, and other media. In particular, modern machine learning systems are trained to convert media items to so-called "vector embeddings", which are short lists of numerical values that encode key information about an item. While vector embeddings would not be…

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.