NSF 19-591: NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS)

Program Solicitation NSF 19-591

		National Science Foundation Directorate for Computer and Information Science and Engineering      Division of Computer and Network Systems      Division of Information and Intelligent Systems      Division of Computing and Communication Foundations Directorate for Engineering      Division of Electrical, Communications and Cyber Systems
		Intel Corporation

Full Proposal Deadline(s) (due by 5 p.m. submitter's local time):

     October 29, 2019

Important Information And Revision Notes

Any proposal submitted in response to this solicitation should be submitted in accordance with the revised NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 20-1), which is effective for proposals submitted, or due, on or after June 1, 2020.

Summary Of Program Requirements

General Information

Program Title:

NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS)

Synopsis of Program:

This program seeks to accelerate fundamental, broad-based research on wireless-specific machine learning (ML) techniques, towards a new wireless system and architecture design, which can dynamically access shared spectrum, efficiently operate with limited radio and network resources, and scale to address the diverse and stringent quality-of-service requirements of future wireless applications. In parallel, this program also targets research on reliable distributed ML by addressing the challenge of computation over wireless edge networks to enable ML for wireless and future applications. Model-based approaches for designing the wireless network stack have proven quite efficient in delivering the networks in wide use today; research enabled by this program is expected to identify realistic problems that can be best solved by ML and to address fundamental questions about expected improvements from using ML over model-based methods.

Proposals may address one or more Research Vectors (RVs): ML for Wireless Networks; ML for Spectrum Management; and Distributed ML over Wireless Edge Networks. It is anticipated that 10 to 15 awards will be made, with an award size of $300,000-$1,500,000, for periods of up to 3 years. The budget should be commensurate with the complexity of the proposed research. Projects will be funded across this range.

Cognizant Program Officer(s):

Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact.

• Alexander Sprintson, Program Director, CISE/CNS, telephone: (703) 292-2170, email: asprints@nsf.gov

• Balakrishnan Prabhakaran, Program Director, CISE/IIS, telephone: (703) 292-4847, email: bprabhak@nsf.gov

• Phillip A. Regalia, Program Director, CISE/CCF, telephone: (703) 292-2981, email: pregalia@nsf.gov

• Anthony Kuh, Program Director, ENG/ECCS, telephone: (703) 292-2210, email: akuh@nsf.gov

• Jenshan Lin, Program Director, ENG/ECCS, telephone: (703) 292-7950, email: jenlin@nsf.gov

• Vida Ilderem, Center Executive Sponsor, Vice President, Intel Labs, telephone: (503) 712-5740, email: vida.ilderem@intel.com

• Shilpa Talwar, Senior Principal Engineer, Intel Labs, telephone: (408) 785-6151, email: shilpa.talwar@intel.com

• Nageen Himayat, Principal Engineer, Intel Labs, telephone: (408) 765-5043, email: nageen.himayat@intel.com

• Jeff Parkhurst, Center Program Director, Intel Labs, telephone: (916) 356-2508, email: jeff.parkhurst@intel.com

Applicable Catalog of Federal Domestic Assistance (CFDA) Number(s):

• 47.041 --- Engineering
• 47.070 --- Computer and Information Science and Engineering

Award Information

Anticipated Type of Award: Standard Grant or Continuing Grant or Intel Agreement (i.e. Contract, Grant or Gift)

Estimated Number of Awards: 10 to 15

It is anticipated that 10 to 15 awards will be made, with an award size of $300,000-$1,500,000, for periods of up to 3 years. The budget should be commensurate with the complexity of the proposed research. Projects will be funded across this range.

Smaller-scale projects are well-suited to one or two investigators (PI and one co-PI or other Senior Personnel) and a small number of students and/or postdocs. Larger-scale projects are well-suited to a small number of investigators (PI, co-PI and/or other Senior Personnel) and several students and/or postdocs. Larger-scale projects are encouraged to be inclusive of a prototyping effort for the researched solutions.

Equal consideration will be provided across all RVs in award decisions.

Anticipated Funding Amount: $9,000,000

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

Eligibility Information

Who May Submit Proposals:

Proposals may only be submitted by the following:

• Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.

Who May Serve as PI:

There are no restrictions or limits.

Limit on Number of Proposals per Organization:

There are no restrictions or limits.

Limit on Number of Proposals per PI or Co-PI: 1

An individual may participate as PI, co-PI, or senior personnel in no more than one proposal submitted in response to this solicitation. In the event that an individual exceeds these limits, the first proposal received will be accepted based on the earliest date and time of proposal submission. No exceptions will be made.

This limit on the number of proposals per PI, co-PI or Senior Personnel applies only to this NSF/Intel Partnership on Machine Learning for Wireless Networking Systems program solicitation.

Proposal Preparation and Submission Instructions

A. Proposal Preparation Instructions

• Letters of Intent: Not required

• Preliminary Proposal Submission: Not required

• Full Proposals:

  • Full Proposals submitted via FastLane: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg.
  • Full Proposals submitted via Research.gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg.
  • Full Proposals submitted via Grants.gov: NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov guidelines apply (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide).

B. Budgetary Information

• Cost Sharing Requirements:

  Inclusion of voluntary committed cost sharing is prohibited.

• Indirect Cost (F&A) Limitations:

  Not Applicable

• Other Budgetary Limitations:

  Not Applicable

C. Due Dates

• Full Proposal Deadline(s) (due by 5 p.m. submitter's local time):

       October 29, 2019

Proposal Review Information Criteria

Merit Review Criteria:

National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information

Award Conditions:

Additional award conditions apply. Please see the full text of this solicitation for further information.

Reporting Requirements:

Additional reporting requirements apply. Please see the full text of this solicitation for further information.

I. Introduction

The next generations of wireless networks¾beyond current cellular and Wi-Fi networks¾are expected to support many diverse services, including real-time autonomous machines, safety-critical health applications, and augmented/virtual reality. Wireless deployments are becoming increasingly dense, hierarchical, and heterogeneous in order to meet the demanding requirements of these services. Machine learning (ML) is emerging as a disruptive technique and architectural framework to intelligently manage the growing complexity and scale of future wireless systems, and to meet the requisite quality of service of future applications. A breadth of recent research results have been reported that apply ML techniques to all layers of the wireless protocol stack [physical layer (PHY), medium access control (MAC), network, applications] and to the end-to-end (E2E) wireless system. The applicability of ML tools is expected to permeate all aspects of wireless communication design, ranging from wireless spectrum management and radio access, to devices and edge infrastructure. At the same time, the increased intelligence in wireless edge networks holds promise for enabling efficient localized computations closer to the source of generated data, i.e., at the wireless edge.

This program seeks to accelerate fundamental, broad-based research on wireless-specific ML techniques, towards a new wireless system and architecture design, which can dynamically access shared spectrum, efficiently operate with limited radio and network resources, and scale to address the diverse and stringent quality-of-service requirements of future wireless applications. In parallel, this program also targets research on reliable distributed ML by addressing the challenge of computation over wireless edge networks to enable ML for wireless and future applications. Model-based approaches for designing the wireless network stack have proven quite efficient in delivering the networks in wide use today; research enabled by this program is expected to identify realistic problems that can be best solved by ML and to address fundamental questions about expected improvements from using ML over model-based methods.

II. Program Description

Wireless networks present unique challenges that require new ML techniques. The fast time-varying statistics of wireless channels and the dynamics of diverse network traffic require ML approaches that can operate online and adapt system parameters in real time. The data sets available for adaptation are typically limited, thus warranting the combination of data-driven ML methods with classical model-based approaches for efficient parameter estimation. In general, ML approaches that integrate domain knowledge, known structure and/or constraints, thus moving from “black box” to “gray/white box” approaches offering interpretable results, hold promise in addressing the aforementioned challenges.

Access to spectrum—licensed, unlicensed, or shared—is crucial to the continued development of wireless systems that can meet the density, throughput, and latency requirements of future applications in a robust and secure manner. Wireless spectrum is managed today in a command-and-control manner where most of the available spectrum is allocated for specific use: scientific, federal, and commercial. However, there are increasing demands on making more spectrum available for commercial use, and most current thinking revolves around the dynamic, shared use of spectrum. Research into the use of novel ML methods that can enable continuous spectrum monitoring, real-time spectrum sharing and coordination, detection of misuse and user-identification in a shared spectrum environment, real-time channel characterization, etc., are highly desired.

In conjunction, complex wireless edge networks also provide highly sophisticated network and client nodes, equipped with rich sensing, computation, and storage resources. In this scenario, there is an opportunity to leverage the wireless network as a distributed “wireless learning engine,” which can use the locally-collected data sets at edge nodes (e.g., video, images, health-related measurements, traffic/crowd statistics) to derive local learning models without sending data to the cloud. Compared to centralized learning solutions, this approach reduces communication costs, improves latency, and preserves data privacy. However, several challenges must be addressed to support ML training and inference at the wireless edge, especially when considering real-time ML. The data collected across edge nodes are not uniformly distributed, and are only representative of partial observations, which makes it difficult for local learning models to provide the accuracy of a global model. Therefore, there is a need for devices to share or infer data or partially computed models to learn the overall global model through device-to-device collaboration or by connecting to a central node. Such collaborative learning requires comprehending unreliable wireless links, deriving fundamental limitations at edge nodes, and adapting for the skewed distribution of data sets. Sharing partially computed models or data sets across the network also raises privacy and security issues that must be addressed.

RESEARCH VECTORS

Proposals may address one or more RVs and the proposed budget must align with the scope of work proposed.

RV1: ML for Wireless Networks

Future applications pose diverse requirements that increase the complexity of wireless systems. As such, optimal model-based designs are generally feasible only in limited scenarios. Furthermore, optimization of individual blocks of the wireless processing chain does not imply E2E optimality of system performance.

Of interest to this program are ML solutions that enable large scale, multi-radio, ultra-dense wireless networks , while addressing issues of massive parameter space dimensionality, partial observability with uncertainty and/or delayed inputs, and stringent Quality of Experience (QoE) requirements. Example topics broadly fall into low-level PHY Layer and signal processing, MAC/Network Layer resource management and routing, E2E Applications and service challenges, as well as support for Network Analytics and Management solutions:

• PHY Layer: novel approaches to air interface design, including coding and modulation; link adaptation for heterogeneous networks; massive Multiple-Input Multiple-Output (MIMO) beamforming and user pairing; channel prediction, localization in millimeter wave (mmWave) systems, and radio frequency (RF) non-linearity mitigation.

• MAC/Network Layer: real-time adaptation techniques for radio resource management to enable intelligent network agents to learn optimal approaches for allocation of power, bandwidth, redundancy, and other resources, as well as multi-radio access load balancing, interference mitigation, user scheduling, mobility management, and routing.

• E2E Applications: service delivery optimizations in heterogeneous network environments, considering a mix of applications with highly-demanding, application-specific QoE requirements, as well as considering the semantics (meaning/context) of information bits being communicated to achieve application QoE.

• Network Analytics and Management: ML techniques for wireless network traffic analysis and prediction to provide service differentiation, visualization, detection of anomalous network conditions, and enhancement of E2E network management and operation.

Beyond network performance enhancements, data-driven ML architectures and principles that can enable systematic and scalable methodology for hardware/software (HW/SW) design of wireless platforms, which do not require substantial re-design with changing requirements, are of significant value.

RV2: ML for Spectrum Management

There is growing interest in applying ML tools that have proven successful in other domains to the problems of spectrum sharing: secure access, dynamic access and monitoring, to name a few. Recent efforts in this area have included the Defense Advanced Research Projects Agency (DARPA) RF Machine Learning Systems (RFMLS) program and the DARPA Spectrum Collaboration Challenge (SC2). These programs have delivered early results showing the promise of using ML approaches in solving specific problems surrounding spectrum usage. However, significant research opportunities in the following areas still need to be explored:

• ML-based Spectrum Monitoring and Analysis: while the focus in the recent past has been on performing spectrum measurements, there is also a need to develop fast, accurate, and robust methods for analyzing large quantities of spectrum data in order to identify opportunities for spectrum sharing, detecting anomalies, and identifying privacy and security concerns.

• Real-time ML Tools for RF Spectrum Sensing and Channel Characterization: techniques for identification of waveforms, latencies, and usage patterns; real-time channel characterization and automatic device configuration using real-time ML.

• Learning-enabled Improved Coexistence: novel methods to support coexistence between commercial (e.g., Wi-Fi, cellular), federal (e.g., US Navy radar), and astronomical (passive radio astronomy observations) users of spectrum.

• ML-based Dynamic Protocol Selection: systems sharing a common spectrum band will often deploy differing MAC protocols; can collaboration be enhanced by using learning-based dynamic protocols?

RV3: Distributed ML over Wireless Edge Networks

ML tasks typically require multi-stage, iterative, non-linear computations, which are susceptible to diverging behavior if interim computations or data to be exchanged during collaboration are untimely or missing. The relevance of the data and the sequence in which data are used also impacts the quality of the overall ML model. The unreliable and dynamically changing wireless connectivity, coupled with mobility and compute constraints of edge devices, creates limitations for the collaboration/coordination required to create globally accurate learning models. This RV seeks to develop an integrated ML and wireless networking framework, emphasizing real-time operation, which tailors wireless system operation and design according to the structure of ML tasks, as well as develops distributed ML approaches that are cognizant of the communication, computation, and architectural features of the wireless edge. Of interest are distributed learning models, such as federated, collaborative, and decentralized learning (e.g., multi-agent learning), for techniques that range from deep neural networks, deep reinforcement learning, self-learning, graph neural networks, and generative networks to new approaches from RV1. Example techniques to achieve the goals outlined may include but are not limited to:

• Exploiting Redundancies in Computation and Communication: low-complexity techniques that minimize or trade off overhead in computations and communication costs to: (a) mitigate the effect of missing/straggling computations during collaboration, and improve response time, convergence behavior, and model accuracy, while addressing privacy; or (b) lower the cost of exchanging data and model parameters, using the broadcast nature of wireless channels and/or effecting “over-the-air” combining of distributed computations. Use of coding techniques to enhance privacy and security of the data, especially considering the broadcast nature of wireless networks, is also of interest.
• Adaptive Computing: techniques that trade off compute accuracy based on application and wireless edge context, thus reaching more efficient utilization of wireless system resources; theoretical tools to predict model accuracy with partial computations, to better adapt workload distribution in edge environments.
• Resource Management for Distributed ML: new optimal/learning-based algorithms to efficiently manage not only communication but also the compute and data resources, while factoring the relevance of the data to the ML model. Techniques should address centralized, distributed, and hybrid wireless systems.
• ML-driven Collaborative Learning Models: use of ML to derive new methods of collaborative learning that address the specific characteristics of the wireless channels are of interest.
• Real-Time Learning and Decision Making: adaptive online ML algorithms that act on dynamically gathered data and make real-time decisions; these decisions would be time-critical. Examples include detecting anomalies such as bad data or malicious attacks on wireless networks.

CREATION OF CURATED DATA SETS

Underlying all three of the above RVs is the need for creating curated data sets and associated tools/interfaces (e.g., in the spirit of Imagenet) that fuel advanced research in ML for wireless networks. These datasets can comprise: (a) parameters pertaining to large-scale, multi-radio, ultra-dense wireless networks; (b) RF signals; and/or (c) relevant computation traces of federated, collaborative, and decentralized learning algorithms that can be used for benchmarking and developing future algorithms for emerging applications.

ASSOCIATED TOOLS

Formal methods including tools for testing and verification of the performance of ML algorithms developed for the above research tasks are also encouraged.

III. Award Information

Anticipated Type of Award: Standard Grant or Continuing Grant or Intel Agreement (i.e. Contract, Grant or Gift)

Estimated Number of Awards: 10 to 15

It is anticipated that 10 to 15 awards will be made, with an award size of $300,000-$1,500,000, for periods of up to 3 years. The budget should be commensurate with the complexity of the proposed research. Projects will be funded across this range.

Smaller-scale projects are well-suited to one or two investigators (PI and one co-PI or other Senior Personnel) and a small number of students and/or postdocs. Larger-scale projects are well-suited to a small number of investigators (PI, co-PI and/or other Senior Personnel) and several students and/or postdocs. Larger-scale projects are encouraged to be inclusive of a prototyping effort for the researched solutions.

Equal consideration will be provided across all RVs in award decisions.

Anticipated Funding Amount: $9,000,000

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

IV. Eligibility Information

Who May Submit Proposals:

Proposals may only be submitted by the following:

• Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.

Who May Serve as PI:

There are no restrictions or limits.

Limit on Number of Proposals per Organization:

There are no restrictions or limits.

Limit on Number of Proposals per PI or Co-PI: 1

An individual may participate as PI, co-PI, or senior personnel in no more than one proposal submitted in response to this solicitation. In the event that an individual exceeds these limits, the first proposal received will be accepted based on the earliest date and time of proposal submission. No exceptions will be made.

This limit on the number of proposals per PI, co-PI or Senior Personnel applies only to this NSF/Intel Partnership on Machine Learning for Wireless Networking Systems program solicitation.

Additional Eligibility Info:

Subawardees may include universities and two-and four-year colleges (including community colleges) accredited in, and having a campus located in, the US acting on behalf of their faculty members; and non-profit, non-academic organizations such as independent museums, observatories, research laboratories, professional societies and similar organizations located in the US that are directly associated with educational or research activities in the computing and information sciences (and closely related fields). Other organizations such as US national laboratories may participate in the proposed activities if they have independent sources of support; they will not be supported by NSF or Intel.

V. Proposal Preparation And Submission Instructions

A. Proposal Preparation Instructions

Full Proposal Preparation Instructions: Proposers may opt to submit proposals in response to this Program Solicitation via FastLane, Research.gov, or Grants.gov.

• Full proposals submitted via FastLane: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg. Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf.gov. Proposers are reminded to identify this program solicitation number in the program solicitation block on the NSF Cover Sheet For Proposal to the National Science Foundation. Compliance with this requirement is critical to determining the relevant proposal processing guidelines. Failure to submit this information may delay processing.
• Full Proposals submitted via Research.gov: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal and Award Policies and Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg. Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf.gov. The Prepare New Proposal setup will prompt you for the program solicitation number.
• Full proposals submitted via Grants.gov: Proposals submitted in response to this program solicitation via Grants.gov should be prepared and submitted in accordance with the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov. The complete text of the NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: (https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide). To obtain copies of the Application Guide and Application Forms Package, click on the Apply tab on the Grants.gov site, then click on the Apply Step 1: Download a Grant Application Package and Application Instructions link and enter the funding opportunity number, (the program solicitation number without the NSF prefix) and press the Download Package button. Paper copies of the Grants.gov Application Guide also may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf.gov.

In determining which method to utilize in the electronic preparation and submission of the proposal, please note the following:

Collaborative Proposals. All collaborative proposals submitted as separate submissions from multiple organizations must be submitted via FastLane or Research.gov. PAPPG Chapter II.D.3 provides additional information on collaborative proposals.

See PAPPG Chapter II.C.2 for guidance on the required sections of a full research proposal submitted to NSF. Please note that the proposal preparation instructions provided in this program solicitation may deviate from the PAPPG instructions.

Proposal Titles:

Proposal titles should begin with “MLWiNS:”, for example, MLWiNS: Title. If you submit a proposal as part of a set of collaborative proposals, the title of the proposal should begin with "Collaborative Research:" followed by "MLWiNS:", for example, Collaborative Research: MLWiNS: Title.

Project Description:

Project Descriptions are limited to 15 pages in length. Proposals must include all sections required by the PAPPG, including Intellectual Merit, Broader Impacts, and Results from Prior NSF Support.

For all collaborative projects, Project Descriptions must be comprehensive and well-integrated, and should make a convincing case that the collaborative contributions of the project team will be greater than the sum of each of their individual contributions.

Supplementary Documents:

In the Supplementary Documents Section, upload the following:

1. A list of Project Personnel and Partner Institutions (Note: In collaborative proposals, the lead organization should provide this information for all participants):

   Provide current, accurate information for all personnel and organizations involved in the project. NSF staff will use this information in the merit review process to manage reviewer selection. The list must include all PIs, co-Pis, Senior Personnel, paid/unpaid Consultants or Collaborators, Subawardees, Postdocs, and project-level advisory committee members. This list should be numbered and include (in this order) Full name, Organization(s), and Role in the project, with each item separated by a semi-colon. Each person listed should start a new numbered line. For example:

   • Mary Smith; XYZ University; PI
   • John Jones; University of PQR; Senior Personnel
   • Jane Brown; XYZ University; Postdoctoral Researcher
   • Bob Adams; ABC Community College; Paid Consultant
   • Susan White; DEF Corporation; Unpaid Collaborator
   • Tim Green; ZZZ University; Subawardee
   
   
2. Collaboration Plans (if applicable; note: in collaborative proposals, the lead institution should provide this information for all participants):

Since the success of collaborative research efforts are known to depend on thoughtful coordination mechanisms that regularly bring together the various participants of the project, all MLWiNS projects that include more than one investigator must include a Collaboration Plan of up to 2 pages. The length and degree of detail provided in the Collaboration Plan should be commensurate with the complexity of the proposed project. Where appropriate, the Collaboration Plan might include: 1) the specific roles of the project participants in all organizations involved; 2) information on how the project will be managed across all the investigators, institutions, and/or disciplines; 3) identification of the specific coordination mechanisms that will enable cross-investigator, cross-institution, and/or cross-discipline scientific integration (e.g., yearly workshops, graduate student exchanges, project meetings at conferences, use of videoconferences, software repositories, etc.); and 4) specific references to the budget line items that support collaboration and coordination mechanisms. If a MLWiNS project with more than one investigator does not include a Collaboration Plan of up to 2 pages, that proposal will be returned without review.

3. Data Management Plan (required):

See Chapter II.C.2.j of the PAPPG for full policy implementation.

For additional information on the Dissemination and Sharing of Research Results, see: https://www.nsf.gov/bfa/dias/policy/dmp.jsp.

For specific guidance for Data Management Plans submitted to the Directorate for Computer and Information Science and Engineering (CISE), see: https://www.nsf.gov/cise/cise_dmp.jsp

Single Copy Documents:

Collaborators and Other Affiliations Information:

Proposers should follow the guidance specified in Chapter II.C.1.e of the NSF PAPPG.

Note the distinction to the first item under Supplementary Documents above: the listing of all project participants is collected by the project lead and entered as a Supplementary Document, which is then automatically included with all proposals in a project. The Collaborators and Other Affiliations are entered for each individual identified as Senior Personnel within each proposal and, as Single Copy Documents, are available only to NSF staff.

B. Budgetary Information

Cost Sharing:

Inclusion of voluntary committed cost sharing is prohibited.

Budget Preparation Instructions:

Budgets for projects should include funding for one or more project representatives (PI/co-PI/senior researcher or NSF-approved replacement) to attend Annual Retreats held after the beginning of the award in the Washington, DC, area, Santa Clara, CA, or Hillsboro, OR.

The budget submitted with the proposal should include all necessary project funds without regard to the two funding organizations; NSF and Intel will inform selected PIs of the breakdown in funding between the two organizations, and will request revised budgets at that point.

C. Due Dates

• Full Proposal Deadline(s) (due by 5 p.m. submitter's local time):

       October 29, 2019

D. FastLane/Research.gov/Grants.gov Requirements

For Proposals Submitted Via FastLane or Research.gov:

To prepare and submit a proposal via FastLane, see detailed technical instructions available at: https://www.fastlane.nsf.gov/a1/newstan.htm. To prepare and submit a proposal via Research.gov, see detailed technical instructions available at: https://www.research.gov/research-portal/appmanager/base/desktop?_nfpb=true&_pageLabel=research_node_display&_nodePath=/researchGov/Service/Desktop/ProposalPreparationandSubmission.html. For FastLane or Research.gov user support, call the FastLane and Research.gov Help Desk at 1-800-673-6188 or e-mail fastlane@nsf.gov or rgov@nsf.gov. The FastLane and Research.gov Help Desk answers general technical questions related to the use of the FastLane and Research.gov systems. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

For Proposals Submitted Via Grants.gov:

Before using Grants.gov for the first time, each organization must register to create an institutional profile. Once registered, the applicant's organization can then apply for any federal grant on the Grants.gov website. Comprehensive information about using Grants.gov is available on the Grants.gov Applicant Resources webpage: https://www.grants.gov/web/grants/applicants.html. In addition, the NSF Grants.gov Application Guide (see link in Section V.A) provides instructions regarding the technical preparation of proposals via Grants.gov. For Grants.gov user support, contact the Grants.gov Contact Center at 1-800-518-4726 or by email: support@grants.gov. The Grants.gov Contact Center answers general technical questions related to the use of Grants.gov. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this solicitation.

Submitting the Proposal: Once all documents have been completed, the Authorized Organizational Representative (AOR) must submit the application to Grants.gov and verify the desired funding opportunity and agency to which the application is submitted. The AOR must then sign and submit the application to Grants.gov. The completed application will be transferred to the NSF FastLane system for further processing.

Proposers that submitted via FastLane or Research.gov may use Research.gov to verify the status of their submission to NSF. For proposers that submitted via Grants.gov, until an application has been received and validated by NSF, the Authorized Organizational Representative may check the status of an application on Grants.gov. After proposers have received an e-mail notification from NSF, Research.gov should be used to check the status of an application.

VI. NSF Proposal Processing And Review Procedures

Proposals received by NSF are assigned to the appropriate NSF program for acknowledgement and, if they meet NSF requirements, for review. All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal. These reviewers are selected by Program Officers charged with oversight of the review process. Proposers are invited to suggest names of persons they believe are especially well qualified to review the proposal and/or persons they would prefer not review the proposal. These suggestions may serve as one source in the reviewer selection process at the Program Officer's discretion. Submission of such names, however, is optional. Care is taken to ensure that reviewers have no conflicts of interest with the proposal. In addition, Program Officers may obtain comments from site visits before recommending final action on proposals. Senior NSF staff further review recommendations for awards. A flowchart that depicts the entire NSF proposal and award process (and associated timeline) is included in PAPPG Exhibit III-1.

A comprehensive description of the Foundation's merit review process is available on the NSF website at: https://www.nsf.gov/bfa/dias/policy/merit_review/.

Proposers should also be aware of core strategies that are essential to the fulfillment of NSF's mission, as articulated in Building the Future: Investing in Discovery and Innovation - NSF Strategic Plan for Fiscal Years (FY) 2018 – 2022. These strategies are integrated in the program planning and implementation process, of which proposal review is one part. NSF's mission is particularly well-implemented through the integration of research and education and broadening participation in NSF programs, projects, and activities.

One of the strategic objectives in support of NSF's mission is to foster integration of research and education through the programs, projects, and activities it supports at academic and research institutions. These institutions must recruit, train, and prepare a diverse STEM workforce to advance the frontiers of science and participate in the U.S. technology-based economy. NSF's contribution to the national innovation ecosystem is to provide cutting-edge research under the guidance of the Nation's most creative scientists and engineers. NSF also supports development of a strong science, technology, engineering, and mathematics (STEM) workforce by investing in building the knowledge that informs improvements in STEM teaching and learning.

NSF's mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering. NSF is committed to this principle of diversity and deems it central to the programs, projects, and activities it considers and supports.

A. Merit Review Principles and Criteria

The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education. To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes." NSF makes every effort to conduct a fair, competitive, transparent merit review process for the selection of projects.

1. Merit Review Principles

These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards. Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply:

• All NSF projects should be of the highest quality and have the potential to advance, if not transform, the frontiers of knowledge.
• NSF projects, in the aggregate, should contribute more broadly to achieving societal goals. These "Broader Impacts" may be accomplished through the research itself, through activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. The project activities may be based on previously established and/or innovative methods and approaches, but in either case must be well justified.
• Meaningful assessment and evaluation of NSF funded projects should be based on appropriate metrics, keeping in mind the likely correlation between the effect of broader impacts and the resources provided to implement projects. If the size of the activity is limited, evaluation of that activity in isolation is not likely to be meaningful. Thus, assessing the effectiveness of these activities may best be done at a higher, more aggregated, level than the individual project.

With respect to the third principle, even if assessment of Broader Impacts outcomes for particular projects is done at an aggregated level, PIs are expected to be accountable for carrying out the activities described in the funded project. Thus, individual projects should include clearly stated goals, specific descriptions of the activities that the PI intends to do, and a plan in place to document the outputs of those activities.

These three merit review principles provide the basis for the merit review criteria, as well as a context within which the users of the criteria can better understand their intent.

2. Merit Review Criteria

All NSF proposals are evaluated through use of the two National Science Board approved merit review criteria. In some instances, however, NSF will employ additional criteria as required to highlight the specific objectives of certain programs and activities.

The two merit review criteria are listed below. Both criteria are to be given full consideration during the review and decision-making processes; each criterion is necessary but neither, by itself, is sufficient. Therefore, proposers must fully address both criteria. (PAPPG Chapter II.C.2.d(i). contains additional information for use by proposers in development of the Project Description section of the proposal). Reviewers are strongly encouraged to review the criteria, including PAPPG Chapter II.C.2.d(i), prior to the review of a proposal.

When evaluating NSF proposals, reviewers will be asked to consider what the proposers want to do, why they want to do it, how they plan to do it, how they will know if they succeed, and what benefits could accrue if the project is successful. These issues apply both to the technical aspects of the proposal and the way in which the project may make broader contributions. To that end, reviewers will be asked to evaluate all proposals against two criteria:

• Intellectual Merit: The Intellectual Merit criterion encompasses the potential to advance knowledge; and
• Broader Impacts: The Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.

The following elements should be considered in the review for both criteria:

1. What is the potential for the proposed activity to
   1. Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and
   2. Benefit society or advance desired societal outcomes (Broader Impacts)?
2. To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?
3. Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?
4. How well qualified is the individual, team, or organization to conduct the proposed activities?
5. Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. NSF values the advancement of scientific knowledge and activities that contribute to achievement of societally relevant outcomes. Such outcomes include, but are not limited to: full participation of women, persons with disabilities, and underrepresented minorities in science, technology, engineering, and mathematics (STEM); improved STEM education and educator development at any level; increased public scientific literacy and public engagement with science and technology; improved well-being of individuals in society; development of a diverse, globally competitive STEM workforce; increased partnerships between academia, industry, and others; improved national security; increased economic competitiveness of the United States; and enhanced infrastructure for research and education.

Proposers are reminded that reviewers will also be asked to review the Data Management Plan and the Postdoctoral Researcher Mentoring Plan, as appropriate.

Additional Solicitation Specific Review Criteria

• How effectively does the proposal address one or more of the program’s three RVs: ML for Wireless Networks; ML for Spectrum Management; and Distributed ML over Wireless Edge Networks?

B. Review and Selection Process

Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review, or Site Visit Review.

Intel and NSF will each conduct separate proposal reviews. For Intel, internal review will be conducted. Proposals and other relevant information about proposals including reviews will be shared between the participating organizations as appropriate. Upon conclusion of the separate reviews, award recommendations will be coordinated by a Joint NSF and Intel Working Group (JWG) comprising personnel from both NSF and Intel. The JWG will recommend meritorious proposals for award.

Reviewers will be asked to evaluate proposals using two National Science Board approved merit review criteria and, if applicable, additional program specific criteria. A summary rating and accompanying narrative will generally be completed and submitted by each reviewer and/or panel. The Program Officer assigned to manage the proposal's review will consider the advice of reviewers and will formulate a recommendation.

After scientific, technical and programmatic review and consideration of appropriate factors, the NSF Program Officer recommends to the cognizant Division Director whether the proposal should be declined or recommended for award. NSF strives to be able to tell applicants whether their proposals have been declined or recommended for funding within six months. Large or particularly complex proposals or proposals from new awardees may require additional review and processing time. The time interval begins on the deadline or target date, or receipt date, whichever is later. The interval ends when the Division Director acts upon the Program Officer's recommendation.

After programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements for review of business, financial, and policy implications. After an administrative review has occurred, Grants and Agreements Officers perform the processing and issuance of a grant or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with a NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

Once an award or declination decision has been made, Principal Investigators are provided feedback about their proposals. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers or any reviewer-identifying information, are sent to the Principal Investigator/Project Director by the Program Officer. In addition, the proposer will receive an explanation of the decision to award or decline funding.

VII. Award Administration Information

A. Notification of the Award

Notification of the award is made to the submitting organization by a Grants Officer in the Division of Grants and Agreements. Organizations whose proposals are declined will be advised as promptly as possible by the cognizant NSF Program administering the program. Verbatim copies of reviews, not including the identity of the reviewer, will be provided automatically to the Principal Investigator. (See Section VI.B. for additional information on the review process.)

Funding Support and Budget Revisions

Proposals selected for joint funding by NSF and Intel will be funded through separate NSF and Intel funding instruments. For each such project, NSF support will be provided via an NSF grant and Intel support will be provided via an Intel agreement (i.e., Contract, Grant, or Gift). Either organization may supplement a project without requiring the other party to provide any additional funds.

The budget submitted with the proposal should include all necessary project funds without regard to the two funding organizations; NSF and Intel will inform selected PIs of the breakdown in funding between the two organizations, and will request revised budgets at that point.

Intel Participation in Research

Intel may separately fund its own personnel to directly participate in NSF/Intel Partnership research, part-time or full-time, with the universities awarded NSF/Intel Partnership projects. Proposals do not need to budget for the cost of such personnel. These Intel researchers will work alongside the academic researchers, identifying opportunities for technology transfer, and being involved with the projects as advisors or as fellow researchers. Optional deployment of Intel Researchers in Residence (RinR) on campuses will require mutual consent by the Parties and respective awardees in the Project Management Plan for each NSF/Intel Partnership award. Further, Intel may designate one of its more senior, separately-funded researchers to work alongside NSF/Intel Partnership academic lead PIs. He/she would inject a perspective on commercial aspects and help with the day-to-day leadership of the center. He/she would also be responsible for working with the Intel Program Director to oversee the engagement of all other Intel researchers.

B. Award Conditions

An NSF award consists of: (1) the award notice, which includes any special provisions applicable to the award and any numbered amendments thereto; (2) the budget, which indicates the amounts, by categories of expense, on which NSF has based its support (or otherwise communicates any specific approvals or disapprovals of proposed expenditures); (3) the proposal referenced in the award notice; (4) the applicable award conditions, such as Grant General Conditions (GC-1)*; or Research Terms and Conditions* and (5) any announcement or other NSF issuance that may be incorporated by reference in the award notice. Cooperative agreements also are administered in accordance with NSF Cooperative Agreement Financial and Administrative Terms and Conditions (CA-FATC) and the applicable Programmatic Terms and Conditions. NSF awards are electronically signed by an NSF Grants and Agreements Officer and transmitted electronically to the organization via e-mail.

*These documents may be accessed electronically on NSF's Website at https://www.nsf.gov/awards/managing/award_conditions.jsp?org=NSF. Paper copies may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from nsfpubs@nsf.gov.

More comprehensive information on NSF Award Conditions and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg.

Special Award Conditions:

Each awarded project will be jointly funded by the NSF and Intel through separate NSF and Intel funding instruments. NSF awards will be made as continuing or standard grants. Intel awards will be made as Intel agreements (i.e., Contracts, Grants, or Gifts). NSF and Intel will manage their respective awards/agreements in accordance with their own guidelines and regulations.

• Site visits, meetings, and annual retreats

Intel and NSF will organize annual retreats for awardees which will bring together the academic community involved in the MLWiNS program, along with NSF and Intel personnel who have interest in the programs. Intel and NSF will work with academic leadership to organize these events. They will involve reviews of the research underway in each project along with presentations from NSF and Intel on technical areas of interest related to each awarded project. Ample time will be provided for face to face interaction between participants in these retreats.

• Intellectual property, publishing, and licensing

All projects agree to distribute all source code that has been authored while working on an NSF/Intel award under a Berkeley Software Distribution (BSD), Apache or other equivalent open source license. Software licenses that require as a condition of use, modification and/or distribution that the software or other software incorporated into, derived from or distributed with the software be licensed by the user to third-parties for the purpose of making and/or distributing derivative works are not permitted. Licenses not appropriate thus include any version of GNU's General Public License (GPL) or Lesser/Library GPL (LGPL), the Artistic License (e.g., PERL), and the Mozilla Public License.

Exceptions to this policy may be granted to address the problem of participation in established open source software projects or standards already licensed under GPL, LGPL, or other copyleft open source licenses.

Projects that generate data or software in performing the work under an award agree not to incorporate any third-party code or background intellectual property, except by separate prearrangement with NSF and Intel, into this data or software that would limit or restrict its ability to be distributed under an open source license.

Awardees may file patent applications, providing that they grant to Intel a non-exclusive, worldwide, royalty-free, sub-licensable license to all intellectual property rights in any inventions or works of authorship resulting from research conducted under the joint award.

• Program management
Intel may require deliverable reports to monitor project progress. Annual on-site reviews may be conducted jointly by NSF and Intel. Intel may lead the organization of monthly or bimonthly (every other month) phone calls with project teams; NSF may participate in these calls at its discretion. NSF and Intel may request visits to the research institutions or may ask PIs to visit NSF or Intel. Intel will support all travel-related expenses for PIs, co-PIs, and students as well as meeting expenses for any additional meetings/retreats (beyond the aforementioned Annual Retreats) that Intel requests and organizes in association with this solicitation.

C. Reporting Requirements

For all multi-year grants (including both standard and continuing grants), the Principal Investigator must submit an annual project report to the cognizant Program Officer no later than 90 days prior to the end of the current budget period. (Some programs or awards require submission of more frequent project reports). No later than 120 days following expiration of a grant, the PI also is required to submit a final project report, and a project outcomes report for the general public.

Failure to provide the required annual or final project reports, or the project outcomes report, will delay NSF review and processing of any future funding increments as well as any pending proposals for all identified PIs and co-PIs on a given award. PIs should examine the formats of the required reports in advance to assure availability of required data.

PIs are required to use NSF's electronic project-reporting system, available through Research.gov, for preparation and submission of annual and final project reports. Such reports provide information on accomplishments, project participants (individual and organizational), publications, and other specific products and impacts of the project. Submission of the report via Research.gov constitutes certification by the PI that the contents of the report are accurate and complete. The project outcomes report also must be prepared and submitted using Research.gov. This report serves as a brief summary, prepared specifically for the public, of the nature and outcomes of the project. This report will be posted on the NSF website exactly as it is submitted by the PI.

More comprehensive information on NSF Reporting Requirements and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg.

Acknowledgement of Support

Awardees will be required to include appropriate acknowledgment of NSF and Intel support in reports and/or publications on work performed under the award. An example of such an acknowledgement would be: "This material is based upon work supported by NSF/Intel Partnership on Machine Learning for Wireless Networking Program under Award Title and No. [Recipient enters project title and awards number(s)]."

VIII. Agency Contacts

Please note that the program contact information is current at the time of publishing. See program website for any updates to the points of contact.

General inquiries regarding this program should be made to:

• Alexander Sprintson, Program Director, CISE/CNS, telephone: (703) 292-2170, email: asprints@nsf.gov

• Balakrishnan Prabhakaran, Program Director, CISE/IIS, telephone: (703) 292-4847, email: bprabhak@nsf.gov

• Phillip A. Regalia, Program Director, CISE/CCF, telephone: (703) 292-2981, email: pregalia@nsf.gov

• Anthony Kuh, Program Director, ENG/ECCS, telephone: (703) 292-2210, email: akuh@nsf.gov

• Jenshan Lin, Program Director, ENG/ECCS, telephone: (703) 292-7950, email: jenlin@nsf.gov

• Vida Ilderem, Center Executive Sponsor, Vice President, Intel Labs, telephone: (503) 712-5740, email: vida.ilderem@intel.com

• Shilpa Talwar, Senior Principal Engineer, Intel Labs, telephone: (408) 785-6151, email: shilpa.talwar@intel.com

• Nageen Himayat, Principal Engineer, Intel Labs, telephone: (408) 765-5043, email: nageen.himayat@intel.com

• Jeff Parkhurst, Center Program Director, Intel Labs, telephone: (916) 356-2508, email: jeff.parkhurst@intel.com

For questions related to the use of FastLane or Research.gov, contact:

• FastLane and Research.gov Help Desk: 1-800-673-6188

  FastLane Help Desk e-mail: fastlane@nsf.gov.

  Research.gov Help Desk e-mail: rgov@nsf.gov

For questions relating to Grants.gov contact:

• Grants.gov Contact Center: If the Authorized Organizational Representatives (AOR) has not received a confirmation message from Grants.gov within 48 hours of submission of application, please contact via telephone: 1-800-518-4726; e-mail: support@grants.gov.

IX. Other Information

The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities. Use of this website by potential proposers is strongly encouraged. In addition, "NSF Update" is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences. Subscribers are informed through e-mail or the user's Web browser each time new publications are issued that match their identified interests. "NSF Update" also is available on NSF's website.

Grants.gov provides an additional electronic capability to search for Federal government-wide grant opportunities. NSF funding opportunities may be accessed via this mechanism. Further information on Grants.gov may be obtained at https://www.grants.gov.

About The National Science Foundation

The National Science Foundation (NSF) is an independent Federal agency created by the National Science Foundation Act of 1950, as amended (42 USC 1861-75). The Act states the purpose of the NSF is "to promote the progress of science; [and] to advance the national health, prosperity, and welfare by supporting research and education in all fields of science and engineering."

NSF funds research and education in most fields of science and engineering. It does this through grants and cooperative agreements to more than 2,000 colleges, universities, K-12 school systems, businesses, informal science organizations and other research organizations throughout the US. The Foundation accounts for about one-fourth of Federal support to academic institutions for basic research.

NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for graduate and postdoctoral fellowships. The agency operates no laboratories itself but does support National Research Centers, user facilities, certain oceanographic vessels and Arctic and Antarctic research stations. The Foundation also supports cooperative research between universities and industry, US participation in international scientific and engineering efforts, and educational activities at every academic level.

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects. See the NSF Proposal & Award Policies & Procedures Guide Chapter II.E.6 for instructions regarding preparation of these types of proposals.

The National Science Foundation has Telephonic Device for the Deaf (TDD) and Federal Information Relay Service (FIRS) capabilities that enable individuals with hearing impairments to communicate with the Foundation about NSF programs, employment or general information. TDD may be accessed at (703) 292-5090 and (800) 281-8749, FIRS at (800) 877-8339.

The National Science Foundation Information Center may be reached at (703) 292-5111.

The National Science Foundation promotes and advances scientific progress in the United States by competitively awarding grants and cooperative agreements for research and education in the sciences, mathematics, and engineering. To get the latest information about program deadlines, to download copies of NSF publications, and to access abstracts of awards, visit the NSF Website at https://www.nsf.gov Location: 2415 Eisenhower Avenue, Alexandria, VA 22314 For General Information (NSF Information Center): (703) 292-5111 TDD (for the hearing-impaired): (703) 292-5090 To Order Publications or Forms: Send an e-mail to: nsfpubs@nsf.gov or telephone: (703) 292-7827 To Locate NSF Employees: (703) 292-5111

Privacy Act And Public Burden Statements

The information requested on proposal forms and project reports is solicited under the authority of the National Science Foundation Act of 1950, as amended. The information on proposal forms will be used in connection with the selection of qualified proposals; and project reports submitted by awardees will be used for program evaluation and reporting within the Executive Branch and to Congress. The information requested may be disclosed to qualified reviewers and staff assistants as part of the proposal review process; to proposer institutions/grantees to provide or obtain data regarding the proposal review process, award decisions, or the administration of awards; to government contractors, experts, volunteers and researchers and educators as necessary to complete assigned work; to other government agencies or other entities needing information regarding applicants or nominees as part of a joint application review process, or in order to coordinate programs or policy; and to another Federal agency, court, or party in a court or Federal administrative proceeding if the government is a party. Information about Principal Investigators may be added to the Reviewer file and used to select potential candidates to serve as peer reviewers or advisory committee members. See System of Record Notices, NSF-50, "Principal Investigator/Proposal File and Associated Records," and NSF-51, "Reviewer/Proposal File and Associated Records.” Submission of the information is voluntary. Failure to provide full and complete information, however, may reduce the possibility of receiving an award.

An agency may not conduct or sponsor, and a person is not required to respond to, an information collection unless it displays a valid Office of Management and Budget (OMB) control number. The OMB control number for this collection is 3145-0058. Public reporting burden for this collection of information is estimated to average 120 hours per response, including the time for reviewing instructions. Send comments regarding the burden estimate and any other aspect of this collection of information, including suggestions for reducing this burden, to:

Suzanne H. Plimpton
Reports Clearance Officer
Office of the General Counsel
National Science Foundation
Alexandria, VA 22314