NSF award 2533946 · SUNY at Buffalo

FDT-BioTech: Uncertainty Quantification in Deep Learning-Driven Digital Twins for Risk-Averse Decisions: Application in Type 1 Diabetes Management

Final report due

29 December 2028

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

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Dates this award owes

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Decide about an extension3 May 2028in 563 days
award ending 2028-08-31 · Research.gov
This one is ours, not NSF’s.
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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How we got this date

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: 2533946
Period of support: 15 Sep 2025 – 31 Aug 2028
Amount: $716,195
Awardee: SUNY at Buffalo, Amherst, NY
Program: MSPA-INTERDISCIPLINARY, OFFICE OF MULTIDISCIPLINARY AC
Directorate: MPS

Abstract

For individuals with Type 1 diabetes, keeping blood sugar levels within a safe range is essential but often difficult due to daily changes in diet, stress, activity, and other factors. This project aims to improve diabetes care by creating virtual models of individual patients--called digital twins--that can learn from wearable health sensors and help guide real-time insulin delivery using automated medical devices.…

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