OARS funding Math & Statistics

We are following up on the 2019 Statistics for Improving Insights and Decisions research awards to foster further innovation in this area and to deepen our collaborations with academia. External researchers can submit proposals to address challenges in applied statistics that have direct applications for producing more effective insights and decisions for data scientists and researchers. Facebook has a large, active and diverse community of practitioners, so we are interested in a varied set of statistical topics, including but not limited to experimentation, forecasting, predictive modeling, survey and ground truth modeling and sampling.

Ethical and Responsible Research (ER2) funds research projects that identify (1) factors that are effective in the formation of ethical STEM researchers and (2) approaches to developing those factors in all STEM fields that NSF supports. ER2 solicits proposals for research that explores the following: "What constitutes responsible conduct for research (RCR), and which cultural and institutional contexts promote ethical STEM research and practice and why?" Do certain labs have a "culture of academic integrity?" What practices contribute to the establishment and maintenance of ethical cultures and how can these practices be transferred, extended to, and integrated into other research and learning settings?" Factors one might consider include: honor codes, professional ethics codes and licensing requirements, an ethic of service and/or service learning, life-long learning requirements, curricula or memberships in organizations (e.g. Engineers without Borders) that stress responsible conduct for research, institutions that serve under-represented groups, institutions where academic and research integrity are cultivated at multiple levels, institutions that cultivate ethics across the curriculum, or programs that promote group work, or do not grade. Successful proposals typically have a comparative dimension, either between or within institutional settings that differ along these or among other factors, and they specify plans for developing interventions that promote the effectiveness of identified factors.

The Jointly Sponsored NIH Predoctoral Training Program in the Neurosciences (JSPTPN) is an institutional program that supports broad and fundamental research training in the neurosciences. In addition to a broad education in the neurosciences, a key component will be a curriculum that provides a strong foundation in experimental design, statistical methodology and quantitative reasoning. JSPTPN programs are intended to be 2 years in duration and students may only be appointed to this training grant during the first 2 years of their graduate research training. The primary objective is to prepare students to be outstanding scientists equipped to pursue careers in neuroscience.

The Bureau of Justice Statistics (BJS) seeks an applicant to conduct the collection, analysis, and dissemination activities for the Annual Surveys of Probation and Parole (ASPP) for the collection years 2020 through 2024. The current funding is for the first 3 years of the award; the final 2 years will be funded upon successful completion of 2020-2022 data. The ASPP are two separate data collections, independently referred to as the Annual Probation Survey and Annual Parole Survey. Since 1980, the ASPP have collected aggregate data on the number of persons supervised on probation or parole (i.e., post-custody community supervision), together referred to as the community supervision population. The ASPP obtain aggregated data from administrative records maintained by state probation and/or parole agencies; local agencies (municipal, county, or court); and the federal system. The ASPP are core BJS data collections and are the only national data collections that describe the size, change, movements, outcomes, and characteristics of the community supervision populations at the national, federal, and state levels. Together with data from the National Prisoner Statistics (NPS) Program, which collects counts of persons incarcerated in federal and state prisons, and data from the Annual Survey of Jails, which collects counts of persons held in local jails, ASPP data are used to estimate the total number of persons supervised by the adult correctional systems in the United States. Collectively, these data collections are also critical for tracking the level and change in the correctional populations over time and enhancing the understanding of the flow of offenders through and eventually out of the criminal justice system.

The Bureau of Justice Statistics (BJS) seeks an agent to conduct data collection and related activities for the National Prisoner Statistics program (NPS) and the National Corrections Reporting Program (NCRP). This award covers the four collection cycles for reporting years 2020 through 2024. The project period is October 1 2020, through September 30, 2025.These two programs were first competed together for the RY 2014-2019 award. The current funding is for the first 3 years of the award; the final 2 years will be funded upon successful completion of 2020-2022 data. The NPS and NCRP are BJS's flagship data collections measuring the size and composition of state and federal prison populations on an annual basis. The two collections complement each other by obtaining aggregate and detailed individual-level information on prisoners, which is used to describe and compare the prison population over time. The NPS collects aggregate counts of the male and female custody and jurisdictional prison populations as of December 31 each year. State departments of corrections (DOCs) and the Federal Bureau of Prisons (BOP) use their administrative records to tally their prison populations by jurisdiction, types of prison admissions and releases during the past year, race/Hispanic origin, and capacity of the facilities that hold prisoners in their custody. NPS also provides annual information on the number of confirmed cases of HIV/AIDS and current testing policies for these conditions. NPS has been collected annually since 1926, and these data are used in BJS's Prisoners series and Corrections Populations in the United States series bulletins.

The Algorithms for Threat Detection (ATD) program will support research projects to develop the next generation of mathematical and statistical algorithms for analysis of large spatiotemporal datasets with application to quantitative models of human dynamics. The program is a partnership between the Division of Mathematical Sciences (DMS) at the National Science Foundation (NSF) and the National Geospatial Intelligence Agency (NGA).

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The National Science Foundation's Directorates for Engineering (ENG), Computer and Information Science and Engineering (CISE), Mathematical and Physical Sciences (MPS), and Geosciences (GEO) are coordinating efforts to identify new concepts and ideas on Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT). A key aspect of this new solicitation is its focus on effective spectrum utilization and/or coexistence techniques, especially with passive uses, which have received less attention from researchers. Coexistence is when two or more applications use the same frequency band at the same time and/or at the same location, yet do not adversely affect one another. Coexistence is especially difficult when at least one of the spectrum users is passive, i.e., not transmitting any radio frequency (RF) energy. Examples of coexisting systems may include passive and active systems (e.g., radio astronomy and 5G wireless communication systems) or two active systems (e.g., weather radar and Wi-Fi). Breakthrough innovations are sought on both the wireless communication hardware and the algorithmic/protocol fronts through synergistic teamwork. The goal of these research projects may be the creation of new technology or significant enhancements to existing wireless infrastructure, with an aim to benefit society by improving spectrum utilization, beyond mere spectrum efficiency. The SWIFT program seeks to fund collaborative team research that transcends the traditional boundaries of individual disciplines.

The Department of Defense (DoD) seeks innovative applications on mechanisms to implement Science, Technology, Engineering, and Mathematics (STEM) education, outreach, and/or workforce initiative programs, here onto will be referred as STEM activities. The Department intends to award multiple grants, subject to the availability of funds. Each individual award will be up to a maximum of $3,000,000, for a period of up to three (3) years. Applications for larger amounts may be considered on a case-by-case basis.

The program is intended to support high-risk theoretical mathematics, physics and computer science projects of exceptional promise and scientific importance on a case-by-case basis. Funding and Allowable Expenses The Targeted Grant in MPS program provides funding for up to five years. The funding level and duration is flexible and should be appropriate based on the type of support requested in the proposal. There is no recommended or assumed funding level for this program. Allowable expenses include: * Up to one month of summer salary and related benefits per year for the PI and any co-Investigator(s). These salary funds are not substitutional (cannot be used to relieve a university of salary costs) and cannot be used to reduce teaching loads below the departmental norm. They can only be used to supplement the salary similarly to a summer salary in the U.S. system. * Domestic or international travel for the PI and co-Investigator(s). * Research equipment, experiments, computations and other expenses directly benefiting the research. * Salary support and related benefits, including tuition support, for staff/research scientists, postdoctoral fellows and research associates, graduate students or undergraduate research assistants.

Reissue of PAR-17-096. The Jointly Sponsored NIH Predoctoral Training Program in the Neurosciences (JSPTPN) is an institutional program that supports broad and fundamental research training in the neurosciences. In addition to a broad education in the neurosciences, a key component will be a curriculum that provides a strong foundation in experimental design, statistical methodology and quantitative reasoning. JSPTPN programs are intended to be 2 years in duration and students may only be appointed to this training grant during the first 2 years of their graduate research training. The primary objective is to prepare students to be outstanding scientists equipped to pursue careers in neuroscience.

The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation's most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization. Activities pursued by early-career faculty should build a firm foundation for a lifetime of leadership in integrating education and research. NSF encourages submission of CAREER proposals from early-career faculty at all CAREER-eligible organizations and especially encourages women, members of underrepresented minority groups, and persons with disabilities to apply.

In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (see https://www.nsf.gov/news/special_reports/big_ideas/index.jsp). The Big Ideas represent unique opportunities to position our Nation at the cutting edge of global science and engineering leadership by bringing together diverse disciplinary perspectives to support convergence research. As such, when responding to this solicitation, even though proposals must be submitted to the Directorate for Engineering (ENG), Office of Emerging Frontiers and Multidisciplinary Activities (ENG/EFMA), once received the proposals will be managed by a cross-disciplinary team of NSF Program Directors.

The Understanding the Rules of Life: Microbiome Theory and Mechanisms (URoL:MTM) program is an integrative collaborationacross Directorates and Offices within the National Science Foundation. The objective of URoL:MTM is to understand and establish the theory and mechanisms that govern the structure and function of microbiomes, a collection of microbes in a specific habitat/environment. This may include but is not limited to host-associated microbiomes, such as those with humans and other organisms, where i) the microbiome impacts host physiology, behavior, development, and fitness; ii) the host influences the metabolic activity, dynamics and evolution of the microbiome, and iii) the environment (biological, chemical, physical, and social) influences and is influenced by both the host and the microbiome. Recent progress has transformed our ability to identify and catalogue the microbes present in a given environment and measure multiple aspects ofbiological, chemical, physical, and social environments that affect the interactions among the members of the microbiome, the host, and/or habitat. Much descriptive and correlative work has been performed on many microbiome systems, particularly those in the human, soil, aquatic, and built environments. This research has resulted in new hypotheses about the microbiome's contributions to potential system function or dysfunction. The current challenge is to integrate the wide range of accumulated data and information and build on them to develop new causal/mechanistic models or theories of interactions and interdependencies across scales and systems.

In 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (see https://www.nsf.gov/news/special_reports/big_ideas/index.jsp). The Big Ideas represent unique opportunities to position our Nation at the cutting edge of global science and engineering leadership by bringing together diverse disciplinary perspectives to support convergence research. As such, when responding to this solicitation, even though proposals must be submitted to the Education and Human Resources (EHR) Directorate/Division of Human Resource Development (HRD), once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors.

Through its Fellowship Programs, the Ford Foundation seeks to increase the diversity of the nation's college and university faculties by increasing their ethnic and racial diversity, maximize the educational benefits of diversity, and increase the number of professors who can and will use diversity as a resource for enriching the education of all students.

The purpose of this Notice of Funding Opportunity (NOFO) is to support innovative research to develop and apply computational tools and mathematical methods for: 1) modeling the spread of pathogens that cause healthcare-associated infections (HAIs) and related antimicrobial resistant (AR) infections; 2) predicting outbreaks of HAI pathogens and trends in the burden of antimicrobial resistant and susceptible HAIs; and 3) investigating the effectiveness of intervention strategies. The models should be developed with the intent that they will be tools for researchers, policymakers, or public health workers who want to better understand and respond to HAIs in the United States. This NOFO will also create a network of leaders in the fields of HAI and AR modeling that will be a resource for informing the development of relevant evidence-based policy. MInD-Healthcare will provide a network of leading modelers to respond to evolving public health needs and emergencies in healthcare settings.

The Division of Molecular and Cellular Biosciences (MCB) has developed a new opportunity to enable researchers with a strong track record of prior accomplishment to pursue a new avenue of research or inquiry. This funding mechanism is designed to facilitate and promote a PI's ability to effective adopt empowering technologies that might not be readily accessible in the PI's current research environment or collaboration network. Transformative research likely spans disciplines and minimizing the practical barriers to doing so will strengthen research programs poised to make significant contributions. The award is intended to allow mid-career or later-stage researchers (Associate or Full Professor, or equivalent) to expand or make a transition in their research programs via a sabbatical leave or similar mechanism of professional development and then develop that research program in their own lab. This award will also enable the PI to acquire new scientific or technical expertise, facilitate the investigator's competitiveness, and potentially lead to transformational impacts in molecular and cellular bioscience.

Transdisciplinary Research In Principles Of Data Science (TRIPODS) aims to bring together the statistics, mathematics, and theoretical computer science communities to develop the theoretical foundations of data science through integrated research and training activities. Phase I, described in solicitation NSF 16-615, supported the development of small collaborative Institutes. Phase II will support a smaller number of larger Institutes, selected from the Phase I Institutes via a second competitive proposal process. All TRIPODS Institutes must involve significant and integral participation by all three of the aforementioned communities.