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Algorithms in the Field (NSF 16-515) - Frequently Asked Questions

What is the Algorithms in the Field grant opportunity?

Algorithms in the Field is an NSF grant opportunity intended to move algorithm research beyond purely theoretical work and into real computing environments where practical constraints matter. It is designed to support projects that connect provable algorithmic ideas with concrete implementations, platforms, or applications.

What is the main goal of the program?

The core goal is to bring together two communities that often work in parallel: (1) theoretical computer science researchers who develop and analyze algorithms with formal properties (such as efficiency, accuracy, approximation guarantees, and performance bounds), and (2) applied or systems-oriented researchers who build and study real systems with real limitations. The program aims to fund tightly integrated collaborations where theory and practice shape each other throughout the project.

What kinds of collaborations does NSF expect under this program?

The solicitation emphasizes close, ongoing collaboration between theory-oriented and systems or application-oriented investigators. The expectation is not simply to run a theory idea on a system at the end, but to design algorithms with real-world constraints in mind and to design systems or applications with principled algorithmic choices backed by formal guarantees.

Is having multiple PIs required?

Yes, the program includes a key leadership requirement: every proposal must include at least two different perspectives in its leadership. Specifically, it must include at least one co-PI with a clear interest in theoretical computer science and at least one co-PI aligned with other areas typically supported by NSF CISE.

What does NSF mean by a "theoretical computer science" perspective?

Based on the solicitation description, the theoretical perspective refers to leadership with a clear interest in developing and proving properties about algorithms, such as efficiency, accuracy, approximation guarantees, and performance bounds under particular computational models.

What does NSF mean by the second leadership perspective (CISE-aligned areas)?

The second perspective is framed broadly and includes research aligned with areas typically supported by NSF CISE, such as computer architecture, programming languages and systems, networks, cyber-physical systems, cyber-human systems (human-in-the-loop settings), and data-driven fields like machine learning, AI and its applications, databases, and data analytics.

Can a proposal be purely theoretical?

The solicitation indicates proposals should not be purely theory efforts packaged as a project. The program is specifically about pushing algorithm research into real computing environments and practical settings, with collaboration that integrates theory and applied or systems work.

Can a proposal be purely systems-oriented?

The solicitation indicates proposals should not be purely systems efforts that treat algorithms as interchangeable parts. The expectation is that algorithmic design choices and system or application design choices inform each other, with algorithmic work grounded in formal reasoning and guarantees.

What kinds of outcomes is NSF looking for beyond academic papers?

The program places weight on dissemination beyond academic publications. Proposals are expected to explain how results will be delivered in usable forms, such as applications, prototypes, tools, languages, compilers, libraries, architectures, system implementations, and datasets.

What does "dissemination beyond publications" mean in practice for a proposal?

From the provided description, it means proposals should describe how the work will be shared in forms that others can adopt, test, extend, or reuse. This may include describing implementation artifacts to be shared, how prototypes or systems will be made available, or how datasets or tools will be delivered to the broader community.

How should a project connect theory-driven guarantees to real environments?

Successful projects would typically describe how provable, theory-driven guarantees will be validated or realized in realistic settings. This implies plans to account for practical constraints and to show how formal algorithmic properties translate when implemented within real systems, platforms, or applications.

What research areas are explicitly within scope for the applied/systems side?

The solicitation lists traditional systems areas (computer architecture, programming languages and systems, networks, cyber-physical systems), human-in-the-loop settings (cyber-human systems), and modern data-driven fields (machine learning, AI and its applications, databases, and data analytics) as examples of areas that can represent the non-theory leadership perspective.

What is the NSF opportunity number for this solicitation?

The opportunity is identified as NSF 16-515.

What are the key dates listed for this opportunity?

The posting and creation date is December 4, 2015. The original and current closing date is March 3, 2016.

What is the maximum award amount listed?

The listed award ceiling is $800,000.

What is the funding type and category described?

Administratively, it is described as a discretionary NSF research grant in the "Science and Technology and other Research and Development" category.

What CFDA number is associated with this opportunity?

The solicitation lists CFDA numbers 47.070.

Who is eligible to apply?

Eligibility is listed as "Others," with additional details referenced in an additional eligibility section in the full solicitation text. Based on the information provided here, the specific eligible entity types are not enumerated beyond that label.

What kind of project would be a strong fit, based on the description?

A strong fit would be a tightly integrated, cross-subfield project where theoretical and applied/systems investigators jointly develop algorithms influenced by real-world constraints, produce concrete deployable artifacts (like prototypes, tools, or implementations), and articulate how the work will influence both the theory community and the relevant systems or domain community.

What is the overarching theme that ties proposals together?

The unifying theme is translating provable algorithmic ideas into concrete, deployable computing artifacts, while using real constraints from systems and applications to motivate new theoretical advances.