To strengthen the connection between the statistical community and National Science Foundation, we continue the series introduced in May 2023 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.

This month’s program officers are Yulia Gel of the Division of Mathematical Sciences in the NSF Directorate for Physical and Mathematical Sciences and Alfred Hero of the Division of Computing and Communication Foundations in the NSF Directorate for Computer and Information Science and Engineering. The awardee responses are from Leah Johnson of Virginia Tech, an award recipient of the NSF Biological Science Directorate.

Alfred Hero is program director in the Division of Computing and Communication Foundations in the NSF Directorate for Computer and Information Science and Engineering. He is on leave from the University of Michigan, where he is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and R. Jamison and Betty Williams Professor of Engineering. Hero was founding co-director of the Michigan Institute for Data Science from 2015–2018, helped launch the Society for Industrial and Applied Mathematics’ Journal on Mathematics of Data Science, and served on the editorial board of the Harvard Data Science Review until 2022. From 2011–2020, he was a member of the Committee on Applied and Theoretical Statistics of the US National Academies of Science, Engineering, and Medicine and chaired the committee from 2018–2020.

Q: What is the Communications and Information Foundations program, and what is its scope?

A: Communications and Information Foundations, referred to as CIF, is a program within the Computing and Communications Foundations Division of the Computer and Information Science and Engineering Directorate at NSF. It supports foundational research on the theoretical underpinnings of information acquisition, transmission, and processing in communications and information processing systems and applications. At its core, CIF is interested in mathematical exploration of novel problem formulations rooted in such applications.

Q: What kind of research proposals does CIF fund out of its core program?

A: Successful CIF proposals often introduce an innovative methodological approach to a compelling application domain, and they always include a substantial mathematical analysis of the method and/or its performance.

CIF has funded novel methods and analyses in areas such as compressive sensing, learning theory, privacy and fairness, decentralized optimization, and information theory. These proposals place their theoretical contribution in the context of impact on communications, signal processing, and image processing applications, which have included areas such as error control coding and data compression, network security, sensor networks, computational chemistry, signal processing on graphs and networks, and computational imaging in medicine and astronomy.

These are just a sampling of research areas CIF has supported, and we are open to novel potentially transformative ideas in other areas. Many successful CIF proposals involve statistical modeling and analysis.

Q: Does the CIF program support PIs whose primary appointment is in a statistics department?

A: Yes. CIF and THE Division of Mathematical Sciences have co-funded collaborative proposals involving PIs from statistics, for example in mathematics of deep learning under the cross-cutting NSF MoDL and SCALE MoDL programs. Furthermore, several of the projects funded by the CIF core program include PIs in statistics—in addition to PIs in computer science, applied math, electrical engineering, and other related fields.

We welcome proposals from researchers in the statistics community who have exciting ideas within the scope of CIF.

Yulia Gel is on leave from The University of Texas at Dallas. This is her third year as a rotator program director of the statistics program of the Division of Mathematical Sciences in the NSF Directorate for Physical and Mathematical Sciences.

Q: What interdisciplinary internship programs exist for graduate students at NSF?

A: NSF offers multiple interdisciplinary internship opportunities for graduate students but we will focus on the Mathematical Sciences Graduate Internship (MSGI) Program and Non-Academic Research Internships for Graduate Students (INTERN).

Mathematical Sciences Graduate Internship
MSGI is the program under the auspices of the NSF Division of Mathematical Sciences. The goal of MSGI is to enrich the training of graduate students in the mathematical sciences by offering an opportunity to participate in internships at federal national laboratories and research facilities. The internships are aimed at students who are interested in understanding the application of advanced mathematical and statistical techniques to real-world problems, regardless of whether the student plans to pursue an academic or nonacademic career.

This program is administered by the Oak Ridge Institute for Science and Education. Each year, the institute publishes a list of participating national labs and short descriptions of projects. Candidates then need to submit their applications through the institute’s system by the deadline.

If selected, the MSGI applicant will receive a stipend of $1,200 per week for living expenses during the 10-week summer internship. In addition, travel reimbursement of inbound and outbound costs up to $2,000 is available for participants who live more than 50 miles one way from the assigned hosting site.

US citizenship is not required for participation in the program. However, depending on the internship assignment, US citizenship or permanent residence may be required.

Non-Academic Research Internships for Graduate Students
The INTERN program is the NSF initiative that involves multiple directorates and divisions. It is open to master’s and doctoral students in statistics, mathematics, or other disciplines who have completed at least one academic year in their graduate program and are making satisfactory progress toward the completion of their degree.

In contrast to the MSGI program, the INTERN program is administered by NSF as a supplement to an active NSF award. That is, the PI/co-PI of an active NSF award (e.g., PhD adviser or other mentor of a graduate student) may request supplemental funding for one or more graduate students to gain knowledge, skills, and experiences that will augment their preparation for a successful long-term career through an internship in a nonacademic setting.

The active NSF award can be administered by DMS or another participating NSF directorate or division. In turn, nonacademic settings are broad, ranging from national labs to museums, nonprofit organizations, industry, and even start-up companies.

The PI/co-PI needs to submit a two-page summary that describes the internship, the student’s résumé, a letter of collaboration from the hosting organization, and budget and other required documents through Research.gov. PIs are encouraged to discuss with the cognizant NSF program director activities that are synergistic with the NSF project scope.

The total amount of funding requested must not exceed $55,000 per student per six-month period. Funds can be used to support travel, tuition and fees, health insurance, and relocation costs for the graduate student. Additionally, up to $2,500 may be used for the PI or graduate research fellow’s adviser to travel to work with the host organization in co-mentoring the student during the internship.

The supplement funding will provide up to six months of support for an internship, and the INTERN project does not need to be performed throughout summer. The target date to submit the INTERN supplement request is April 15 for each fiscal year.

In addition to the more general INTERN program, NSF runs multiple inter-agency INTERN initiatives such as Research Internships for Graduate Students at Air Force Research Laboratory, Geothermal INTERN joint with the US Department of Energy Office of Energy Efficiency and Renewable Energy, and Graduate Research Internships in Forensic Science and Criminal Justice Contexts. See the detailed description and all specific conditions of each INTERN program at the NSF website.

Leah R. Johnson is an associate professor in the department of statistics at Virginia Tech. She earned her PhD at the University of California, Santa Cruz in 2006. Her research interests are in statistical ecology, vector-borne diseases, and methodology for inference in complex models of biological systems. She was a recipient of an NSF CAREER award in 2018.

Johnson was recently funded through the NSF Division of Biological Infrastructure for “CIBR: VectorByte: A Global Informatics Platform for Studying the Ecology of Vector-Borne Diseases,” a five-year collaborative grant. The project has three PIs/institutions, the other two being Sadie J. Ryan (University of Florida) and Samuel Rund (University of Notre Dame). They also have an unfunded collaborator, Samraat Pawar (Imperial College London). The total award amount across the three locations is approximately $2 million.

The main goals of this project are to build a centralized open-access data platform called VectorByte, provide open-access tools to explore and use the data, and enable training workshops. Thus, funding supports multiple postdocs and graduate students, database building, training workshops, and tool development.

Johnson has been submitting proposals for approximately 10 years to DMS, DBI, and the Division of Environmental Biology. She regularly serves on NSF review panels or ad hoc reviews for the three divisions.

Q: What will the proposal accomplish?
A: In this project, we are establishing a global open-access data platform to support the study of disease vectors.

Yearly, vector-borne diseases account for 17% of human infectious diseases and billions of dollars in crop and livestock losses. To better prevent and predict outbreaks of vector-borne diseases requires that information and data on interactions of vectors with their environments over space and time be combined. However, efforts to do this have been hindered by the isolation of data collected on vectors, difficulty in data accessibility, and disparate data formats.

This project will follow FAIR data principles to bring biological trait and abundance data for human and non-human disease vectors into a centralized repository. It will also provide analysis tools and training to a wide audience of researchers and practitioners.

Q: 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.
A: For an applied statistician like me, I have found cross-cutting or broad calls are often better fits for much of my collaborative work. So, a few years ago when I was invited to sit on a panel for the Division of Biological Infrastructure, I jumped at the chance. At the time, I was unfamiliar with the division or any of its programs. I learned a lot about opportunities under this call during that time and across subsequent panels. Sitting on panels is useful generally if you are submitting proposals to NSF, and even more so for calls you’re less familiar with. Program officers love to have volunteers, so let them know you’re willing!

Q: What advice do you have for others applying for NSF funding?
A: Although it can be a bit intimidating at first, setting up a time to talk to the program officers about whether your ideas fit into a particular call is enormously useful. There is already a lot of stochasticity in the grant review process. Making sure your grant is a good fit increases your odds substantially. Nearly all my successful proposals started with a conversation with a program officer.

Additionally, making sure to take the time to make your project easy to read/follow is important. Your reviewers read a lot of proposals, and they are unlikely to be familiar with your exact area of research. Clear writing with goals and objectives that are easy to understand will also improve your odds.