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Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs

NSF

closed
OpenLast verified: 2026-06-20

About This Grant

Software bugs can have disastrous consequences, ranging from financial costs to loss of human life. As a result, for high-stakes systems, software vendors are increasingly applying techniques that can prove the absence of various kinds of bugs. However, existing techniques have limitations that make them inapplicable for certain types of programs that make use of randomness, which is common in sensitive software domains such as cryptography and machine learning. This project will develop new techniques for reasoning about randomness in programs, which will make it possible to prove important properties about these programs, thereby improving software quality in these critical areas. In addition, the team of researchers will develop educational materials to make the project's ideas more broadly accessible to students, researchers, and industrial practitioners. This project targets programs that exhibit two important kinds of effectful features: concurrency and randomization. Existing formal verification techniques cannot handle the complexity and expressivity of many programming language features, and these features make it harder to write, test, and reason about programs. Establishing correctness in the presence of just one of these features is hard enough, and it only becomes more difficult when they are combined. This project will develop program logics and reasoning tools that can enable more precise, compositional analysis of concurrent randomized programs by building on a new semantic model of randomness and concurrency. The investigators will formally verify the soundness of the logic and build a framework for using it inside of an interactive theorem prover. This formalized framework will facilitate further breakthroughs in verification of concurrent randomized programs in different domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Grant Summary

Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs is a NSF grant providing up to $334K for university, nonprofit, small business. Applications are due 2029-09-30 (open). Check eligibility and apply with FindGrants.

Focus Areas

machine learningeducation

Eligibility

universitynonprofitsmall business

How to Apply

Funding Range

Up to $334K

Deadline

2029-09-30

Complexity
Medium
  1. 1Confirm your organization is eligible for Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs from NSF, checking organization type, location, and any population or project requirements.
  2. 2Gather the required documents and information, including your organization details, project plan, and budget figures.
  3. 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
  4. 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NSF before the deadline.
This record is a past award, contract, or funder profile — useful for research, but not an open grant application. Check the original source for current opportunities from this funder.

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Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs: Frequently Asked Questions

Who is eligible for the Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs?

Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs is offered by NSF and is generally open to university, nonprofit, small business. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.

How much funding does the Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs provide?

Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs provides up to $334K per award from NSF. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.

When is the Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs deadline?

Applications for Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs are due 2029-09-30 (open). Because deadlines can change, verify the date with the funder, NSF, and give yourself enough time to prepare a complete, competitive application before the close date.

How do you apply for the Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs?

To apply for Collaborative Research: SHF: Medium: Probabilistic Concurrent Outcome Logic (pcOL) -- Expressive Specifications for Concurrent Randomized Programs, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NSF.