NSF award 2542902 · Johns Hopkins University

CAREER: Structural and Algorithmic Foundations of Variational Inequalities in Machine Learning: From Principles to Scalability

Final report due

28 November 2031

in 1,867 days · The award ends on 31 July 2031.

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award ending 2031-07-31 · Research.gov
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Extension request16 Jun 2031in 1702 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Jul 2031in 1737 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Nov 2031in 1867 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Nov 2031in 1867 days
award ending 2031-07-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Nov 2031in 1867 days
award ending 2031-07-31 · 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 July 2031.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 November 2031.

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Award card

Award number: 2542902
Period of support: 1 Aug 2026 – 31 Jul 2031
Amount: $624,665
Awardee: Johns Hopkins University, Baltimore, MD
Program: Comm & Information Foundations
Directorate: CSE

Abstract

Many important recent advances in machine learning and data science involve systems with several interacting parts. In such settings, problems are often formulated as multi-person or multi-objective systems, in which several models, agents, or decision makers pursue interacting objectives. Unlike traditional optimization settings, where the goal is to minimize a single objective function, these problems often seek a…

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