NSF award 2617314 · SUNY at Binghamton

Collaborative Research: III: Foundations for Reliable and Generalizable Spatial-Temporal Large Language Models in Urban Dynamics

Final report due

29 December 2029

in 1,168 days · The award ends on 31 August 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 extension3 May 2029in 928 days
award ending 2029-08-31 · Research.gov
This one is ours, not NSF’s.
Extension request17 Jul 2029in 1003 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2029in 1038 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2029in 1168 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2029in 1168 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2029in 1168 days
award ending 2029-08-31 · 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 August 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 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: 2617314
Period of support: 1 Sep 2026 – 31 Aug 2029
Amount: $300,000
Awardee: SUNY at Binghamton, Binghamton, NY
Program: Info Integration & Informatics
Directorate: CSE

Abstract

Urban dynamics prediction is increasingly powered by Large Language Model (LLM)-based spatial-temporal forecasting models. This approach includes multi-sensor observations (e.g., traffic speeds and travel demand) being tokenized into sequential representations and processed by LLMs to generate numerical forecasts. Although this paradigm has substantially improved predictive accuracy on benchmark datasets, accuracy…

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.