NSF award 2621546 · William Marsh Rice University

Collaborative Research: Unlocking Complex Heterogeneity in Large Spatial-Temporal Data with Scalable Quantile Learning

Final report due

29 December 2028

in 803 days · The award ends on 31 August 2028.

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award ending 2028-08-31 · Research.gov
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Extension request17 Jul 2028in 638 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2028in 673 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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NSF lists this award's end date as 31 August 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2028.

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Award card

Award number: 2621546
Period of support: 1 Aug 2026 – 31 Aug 2028
Amount: $264,740
Awardee: William Marsh Rice University, Houston, TX
Program: CDS&E-MSS, CDS&E, STATISTICS
Directorate: MPS

Abstract

Advancements in modern technology have exponentially increased the availability and complexity of spatial and spatiotemporal data across various fields, presenting unique challenges and opportunities. This project aims to develop scalable and efficient quantile learning techniques to unlock valuable insights from large-scale, heterogeneous spatial-temporal data, overcoming limitations in handling dynamic patterns…

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