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Insights from Program Officers, Awardee on Funding and Research Strategies

1 August 2023 393 views No Comment
To strengthen the connection between the statistical community and National Science Foundation, we continue the series introduced in the May 2023 issue of Amstat News that poses questions to NSF program officers and awardees. If you have questions or comments for the program officers, send them to ASA Director of Science Policy Steve Pierson.

    NSF Statistics Program Directors

    Yulia Gel, Edsel Peña, Yong Zeng, and Jun Zhu collectively responded to the following questions: 

    When a proposal is declined for funding, is the principal investigator (PI) allowed to submit a revised proposal?

    A declined proposal may be resubmitted, but only after it has undergone substantial revision.

    NSF programs that accept proposals at any time may have established guidelines in which a declined proposal (or reasonable facsimile of that proposal/topic by the same PI and co-PIs, where applicable) is ineligible for resubmission for a specified period. This moratorium allows PIs/co-PIs sufficient time to digest the results of the merit review and revise/restructure the declined proposal accordingly.

    A proposal the program considers too similar to a previous proposal under the moratorium period may be returned without review. A resubmitted proposal that has not clearly taken into account the major comments or concerns resulting from the prior NSF review may be returned without review. The foundation will treat the revised proposal as a new proposal, subject to the standard review procedures.

    What is the difference between a standard award and a continuing award?

    For standard grants, NSF agrees to provide a specific level of support for a specified period with no statement of the NSF’s intent to provide additional future support without submission of another proposal (see NSF Proposal and Award Policies and Procedures Guide, Page xiv).

    On the other hand, continuing grants are those for which NSF agrees to provide a specific level of support for a specified period of time—usually a year—with a statement of intent to provide additional support of the project for additional periods, provided funds are available and the results achieved warrant further support (see NSF Proposal and Award Policies and Procedures Guide, Page xiv).

    In both award instruments, the PI is required to submit annual progress reports. For continuing grants, the assessment of whether the “results achieved warrant further support” are based on these reports. The continuing grant increment—the funds for the next funding period for a continuing grant—is released by the cognizant program officer upon approval of the annual report.

    NSF program officers decide the type of award instrument to use, whether standard or continuing, based on considerations such as the career stage of the PI and the funds available for the program.

    See also Chapter VI of the NSF Proposal and Award Policies and Procedures Guide.

    NSF Awardee

    Photo of Mikyoung Jun, shoulder length dark hair, smilingMikyoung Jun, professor of mathematics and ConocoPhillips Data Science Professor at the University of Houston, is the PI on an NSF grant from the Division of Information and Intelligent Systems in the NSF Directorate for Computer and Information Science and Engineering. Her program supports multi-institutional efforts to provide data science training for the future energy industry workforce in the greater Houston area consistent with a transition to a more sustainable and cleaner environment.

      From 2005–2020, Jun was a professor in the department of statistics at Texas A&M University. She earned a BS and PhD in statistics from Seoul National University and The University of Chicago, respectively. Jun has a history of research funding support from the National Science Foundation as a PI and co-PI, though this is her first grant for training.

      How will the funding be used?

      The program consists of year-long training components for undergraduate and master’s students with no prerequisites. It includes a summer boot camp, team research projects, and a summer internship. The University of Houston main campus leads the effort, with UH downtown, UH Victoria, UH Clear Lake, and Sam Houston State University following. We have various energy industry partners providing seminars, career advice, and data sets for research projects. The grant is $1.5 million for three years, and a large portion of that is for students’ stipends.

      Summarize what the project will accomplish.

      The project will produce workforce-ready data scientists with the right skill sets for the energy industry to facilitate energy transition in the region and across the nation. Given the emphasis of the participating universities in diversity and equity, the proposed program will provide opportunities for minorities and underrepresented groups. Developed education materials will be made public and be freely available. Results of student team projects will be useful for the energy industry and broader community dealing with similar data structures. Selected student teams from the research project will present their results at conferences for further dissemination.

      If an NSF non-DMS entity partially or fully funded the award, please describe your approach to that entity so others might learn from it.

      This project belongs to NSF’s Harnessing the Data Revolution Big Ideas program, which is a joint program with entities such as the following:

      • Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences
      • Division of Information and Intelligent Systems in the Directorate for Computer and Information Science and Engineering
      • Division of Social and Economic Sciences in the Directorate for Social, Behavioral, and Economic Sciences
      • Office of Polar Programs in the Directorate for Geosciences
      • Division of Undergraduate Education, Directorate for STEM Education
      • Division of Civil, Mechanical, and Manufacturing Innovation, Directorate for Engineering

      Our project covers a wide range of fields, including statistics/mathematics, computer science and engineering, geoscience, and public policy. The diverse expertise of the personnel leading our project aligns well with the Harnessing the Data Revolution vision of NSF.

      What advice do you have for others in applying for NSF funding?

      First, read the NSF solicitation carefully and make sure you are addressing all the important components in the program. Second, talk to the program officers whenever you have an opportunity and discuss your ideas with them. If multiple institutions are involved, start early in terms of budget and other arrangements, as each institution may work differently and there is a universal problem of not enough staff that may result in delays.

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