NSF award 2607870 · University of Pennsylvania

Collaborative Research: Elements: Software and algorithms to advance likelihood-based inference for partially observed dynamic systems via differentiation and parallelization

Final report due

28 October 2029

in 1,106 days · The award ends on 30 June 2029.

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No-cost extension notice20 Jun 2029in 976 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Oct 2029in 1106 days
award ending 2029-06-30 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Oct 2029in 1106 days
award ending 2029-06-30 · 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 30 June 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 October 2029.

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Award card

Award number: 2607870
Period of support: 1 Jul 2026 – 30 Jun 2029
Amount: $297,157
Awardee: University of Pennsylvania, Philadelphia, PA
Program: Software Institutes, Special Initiatives, STATISTICS
Directorate: CSE

Abstract

Automatic differentiation is a powerful computational technique that has driven recent advances in artificial intelligence and machine learning. This project will develop automatic differentiation methods for partially observed Markov process (POMP) models, which are widely used by scientists to investigate nonlinear stochastic dynamic systems such as infectious disease transmission, ecological dynamics, and other…

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