Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&sort=-award_amount
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The REU program has both scientific and societal benefits integrating research and education. This REU Site award to University of Tennessee Chattanooga, located in Chattanooga, TN, and Southern Illinois University Edwardsville in Edwardsville, IL, will support the training of 10 students for 10 weeks for three years. Research is conducted at Chattanooga, TN, Edwardsville, IL, and Cleveland, TN. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities or from an under-represented group, will be trained in the program. Students will learn how policy decisions are made and how interdisciplinary research is conducted, with many presenting the results of their work at scientific conferences and to local policymakers and community stakeholders. The research will integrate greenway networks into the broader field of urban science and improve our understanding of the environmental and human associated impacts of greenway networks in urban areas. Assessment of the program will be done through the online SALG URSSA tool. Students will be tracked after the program in order to determine their career paths.The theme for this 3-year research experience focuses on enhancing environmental resilience and sustainability by examining the interaction between human and natural systems within urban greenway networks. The research is grounded in three fundamental questions: 1) What are the human and ecological drivers of microclimate? 2) What are the human usage patterns in urban greenway networks? 3) How can empirical evidence on urban greenway dynamics inform the broader scientific community and local community stakeholders on ways to mitigate environmental impacts and social disparities in cities? Students will work in interdisciplinary teams and assess how greenways vary based on social and ecological characteristics of the greenway in each respective city. Results from each team will be combined into a larger dataset which will be used to assess greenway characteristics in varying city sizes. The findings will be shared with community leaders and stakeholders to inform them of the current impacts of their greenway system and potential opportunities for expanding the greenway network. Students will learn and apply skills related to: biodiversity; data collection, analysis, and visualization; geographic information systems (GIS); and, evidence-based policymaking. These skills will then be used to better understand the human and biological drivers of microclimate variation within greenway networks and inform policymakers as to the best ways to mitigate environmental impacts and social disparities in order to create more resilient cities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12561", "attributes": { "award_id": "2336852", "title": "I-Corps: A Low-cost and Non-contact Respiration Monitoring Method for COVID-19 Screening and Prognosis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 1763, "first_name": "Sabit", "last_name": "Ekin", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 387, "ror": "https://ror.org/01g9vbr38", "name": "Oklahoma State University", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the ability to monitor respiration using a low-cost and non-contact sensing method. Studies show that real-time health monitoring devices will reach a market value of over $65 billion by 2022. Given the high prevalence of lifestyle-associated disorders, long-term continuous monitoring of physiological parameters becomes important for many healthcare cases such as apnea and for human-computer-interaction applications. The anticipated benefits of the technology in the current COVID-19 outbreak include, but are not limited to, helping to reduce the load of current (expensive and limited) respiration monitoring medical equipment, being deployable in open-spaces and being highly desirable for the drastically increasing numbers of COVID-19 patients. Further, since respiration monitoring is a ubiquitous element of medicine, this work may also impact the entire health care community, from patients in their homes, to doctor’s offices, to large medical institutions and industries. This I-Corps project involves the technological advancement required to enable the proposed low-cost and non-contact respiration sensing method. This method represents a substantial departure from traditional approaches to wireless respiration monitoring and is poised to make significant contributions in this area. The proposed technology is timely given the critical worldwide impact of COVID-19. The proposed solution is adequately deployable in home environments (e.g. living rooms) and hospitals, etc. to remotely monitor respiration for COVID-19 screening and prognosis. The proposed approach allows very low-cost, safe, easy, continuous, and non-obtrusive gathering of respiration data — a critical input for cost-effective and proactive treatment and management of subjects with COVID-19 and other chronic respiratory conditions. This project will allow the team to better understand the unmet needs by conducting customer discoveries and interviews, develop a viable business model, and learn the desired features for developing a compelling minimum viable product.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12562", "attributes": { "award_id": "2229100", "title": "Collaborative Research: SHINE: Observational and Theoretical Studies of the Parametric Decay Instability in the Lower Solar Atmosphere", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "PLANETARY ASTRONOMY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28489, "first_name": "Michael", "last_name": "Hahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "It is unclear how the outer layer of the Sun, the corona, is heated to millions of degrees. One potential mechanism is through the parametric decay instability (PDI). This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the solar corona through numerical simulations and analysis of space-based and NSF-funded ground based solar observations. The project also supports outreach activities in New York City and New Mexico. Two post-doctoral researchers will be supported, along with undergraduate students at Columbia University.The project will determine whether PDI is an important process in the solar atmosphere. The team will analyze observations from the Coronal Multichannel Polarimeter, the Extreme Ultraviolet Imaging Spectrometer, the Interface Region Imaging Spectrometer, and the NSF-funded Daniel K. Inouye Solar Telescope. Numerical modeling will be used to better understand the behavior of PDI in the presence of gradients in the plasma properties of the transition region and the corona. Magnetohydrodynamic simulations will be carried out to study Alfven waves and PDI growth in the lower solar atmosphere, where the scale length of gradients is smaller than the wavelength. This is critical to understand how density fluctuations are generated very close to the Sun. Moreover, ion heating associated with acoustic waves produced by PDI will be investigated using hybrid simulations, to estimate what fraction of the Alfven wave power can be dissipated in the lower atmosphere.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12563", "attributes": { "award_id": "2312006", "title": "CRII: CNS: Integrating Security Tasks into Multicore Real-Time Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CSR-Computer Systems Research" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28490, "first_name": "Monowar", "last_name": "Hasan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 306, "ror": "https://ror.org/05dk0ce17", "name": "Washington State University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Many critical systems of modern society (e.g., engine control units in automobiles, logic controllers in manufacturing and power plants, aircraft control and navigation systems, industrial control systems, sensing and perception systems in robot-aided healthcare) have \"real-time\" (i.e., strict timing and safety) requirements. Emerging Internet-of-things-specific applications (e.g., connected autonomous cars, unmanned aerial vehicles), the trending use of off-the-shelf components, and widespread Internet/network connectivity expand the possibility of security breaches in those critical systems, as revealed by recent real-world attacks. The key innovation of this research is the development of a unified framework to integrate monitoring and detection mechanisms as first-class elements within the design of real-time systems, especially those built with multicore chips. This project will (a) devise novel algorithms, scheduling models, and frameworks to integrate security into multicore platforms that are cognizant of real-time requirements, (b) build design-time tools and system-level plugins to incorporate the proposed techniques into off-the-shelf systems, and (c) develop metrics to carefully trade-off two contending requirements: timeliness and security. The ideas will be validated through experimentation and testing on two off-the-shelf platforms: a multi-terrain rover and a six-degree-of-freedom robotic arm.This research will advance the field by enabling system designers to better understand how to integrate security concerns, with a focus on revealing security trade-offs to ensure minimal (or no) perturbations on real-time properties. Techniques developed as part of this project will make safety-critical, real-time systems more secure and applicable to various domains (e.g., automobiles, avionics, drones, space rovers, power grids, manufacturing plants, medical devices, industrial control systems). The proposed research and educational plans will enhance the knowledge of the next-generation technological workforce in cyber-physical systems and cyber-security. This award supports the training of graduate and undergraduate students, the development of a new security course at Wichita State University, and the integration of research findings into educational materials. All hardware, software, and system implementations (including documentation and tutorials) will be freely available in a public repository (https://github.com/CPS2RL) for educators, scientists, industry personnel, and hobbyists to access and use.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12564", "attributes": { "award_id": "2230482", "title": "Modeling Dynamics and Impacts of a new class of Kelvin-Helmholtz Instabilities that Drive Enhanced Turbulence and Mixing in the MLT", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "AERONOMY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28491, "first_name": "Tyler", "last_name": "Mixa", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2115, "ror": "", "name": "GLOBAL ATMOSPHERIC TECHNOLOGIES AND SCIENCES, INC.", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "State-of-the-art general circulation models (GCMs) used for weather and climate prediction underestimate the amount of turbulent mixing in the middle atmosphere by up to a factor of 2, and as a result, mischaracterize the transport and global distributions of CO2 and other primary atmospheric constituents. GCMs attribute mixing to a single dynamical source that neglects newly discovered, small-scale turbulent processes thought to be widespread, and perhaps even ubiquitous, in the middle atmosphere and beyond. This project will identify these unique “tube and knot” (T&K) instability dynamics and their implications for mixing through observationally guided, high-resolution modeling studies. Sophisticated turbulence and chemical analysis capabilities will be employed to address scientific goals among a diverse range of atmospheric research communities. The resulting knowledge of T&K-driven momentum transport and deposition will aid the development of improved mixing parameterizations in GCMs and yield higher accuracy weather and climate forecasting to address a critical societal need. It will also support the education of a University of Colorado Boulder graduate student and a Utah State University undergraduate student while facilitating outreach events that promote climate science education to under-represented pre-college students in the surrounding communities.This project will identify and quantify Kelvin Helmholtz instability (KHI) T&K dynamics and implications for mixing in the MLT via high-resolution modeling, utilizing the unique capabilities of in-house models CGCAM and SAM to characterize instability dynamics extending to turbulence scales and mixing in deep domains with realistic environments. The results will improve mixing parameterization schemes in weather and climate models by addressing GCM underestimation of the eddy diffusion coefficient Kzz. The goals of this research are to identify and quantify the large-scale (mean and tidal) and GW environments that enable KHI T&K dynamics, and account for their spatial scales and intensities; to quantify the diversity of KHI T&K dynamics, and their implications for energy dissipation, mixing, and influences in the MLT via high-resolution modeling; and to employ our KHI T&K modeling to assess their enhancements of energy dissipation rates, mixing, and implied Kzz relative to those expected for GW breaking. The analysis and modeling approach addressing these research goals will employ KHI T&K observations by USU Advanced Mesospheric Temperature Mapper (AMTM) OH airglow imaging and GATS SAAMER radar and lidar profiling of winds, temperatures, and Na densities in Tierra del Fuego, Chile, and Poker Flat, Alaska, to guide representative modeling environments (e.g., GW and tidal shears, multi-scale superpositions). Informed by these observations, a wide range of KHI T&K simulations will be performed to capture the diversity of responses for varying environmental conditions and evaluate KHI T&K mixing, enabling definition of the parameters dictating a KHI Kzz for representative shear layer scales and Richardson and Reynolds numbers. This research will directly result in a better understanding of unresolved mixing dynamics in the MLT and how they impact constituent particles and energy transport to higher levels of the atmosphere.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12565", "attributes": { "award_id": "2222289", "title": "Reimagining Grading to Support Nontraditional and Rural Students in High Enrollment, Gateway STEM Courses", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Postdoctoral Fellowships" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28492, "first_name": "Tara", "last_name": "Slominski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 340, "ror": "", "name": "North Dakota State University Fargo", "address": "", "city": "", "state": "ND", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Common classroom practices, such as grading and the use of grades to assess knowledge and performance, may have unintended consequences on students who invariably derive an awareness of their own academic abilities from the results of those grading structures. In fact, these traditional practices may inadvertently create and promote inequities among different student groups, particularly in large enrollment courses, but these issues have largely been unexplored. This postdoctoral research fellowship project seeks to examine the impact of grading practices on self-concept and STEM persistence with a special focus on rural and nontraditional students. The project has promise to produce new insights about equitable classroom and grading practices for rural and nontraditional students that are compatible with the constraints of high enrollment gateway courses. In addition to conducting the research project, the investigator will build STEM education research capacity in quantitative methods and approaches through an explicit professional development plan.This project's research hypothesis is that nontraditional and rural students are especially vulnerable to the limitations of traditional grading when used in high enrollment, gateway STEM courses. The investigator will use a mixed-methods, community-based participatory research approach with three objectives. First is to explore the relationship between grading structures and nontraditional and rural students’ self-concept and intentions to persist in high enrollment, gateway STEM courses. Second is to characterize how aspects of traditional and alternative grading schemes influence nontraditional and rural students’ academic self-concept and intentions to persist in STEM. Third is to develop empirically supported approaches of equitable grading strategies that are conducive to high enrollment STEM classrooms and are particularly supportive for rural students and students that identify as nontraditional. The findings could provide faculty with practical assessment and grading approaches to address the systemic inequitable practices associated with grading and create more equitable learning environments for students broadly.The project responds to the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12566", "attributes": { "award_id": "2229101", "title": "Collaborative Research: SHINE: Observational and Theoretical Studies of the Parametric Decay Instability in the Lower Solar Atmosphere", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "SOLAR-TERRESTRIAL" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28493, "first_name": "Xiangrong", "last_name": "Fu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 606, "ror": "https://ror.org/01qnpp968", "name": "New Mexico Consortium", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true }, "abstract": "It is unclear how the outer layer of the Sun, the corona, is heated to millions of degrees. One potential mechanism is through the parametric decay instability (PDI). This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the solar corona through numerical simulations and analysis of space-based and NSF-funded ground based solar observations. The project also supports outreach activities in New York City and New Mexico. Two post-doctoral researchers will be supported, along with undergraduate students at Columbia University.The project will determine whether PDI is an important process in the solar atmosphere. The team will analyze observations from the Coronal Multichannel Polarimeter, the Extreme Ultraviolet Imaging Spectrometer, the Interface Region Imaging Spectrometer, and the NSF-funded Daniel K. Inouye Solar Telescope. Numerical modeling will be used to better understand the behavior of PDI in the presence of gradients in the plasma properties of the transition region and the corona. Magnetohydrodynamic simulations will be carried out to study Alfven waves and PDI growth in the lower solar atmosphere, where the scale length of gradients is smaller than the wavelength. This is critical to understand how density fluctuations are generated very close to the Sun. Moreover, ion heating associated with acoustic waves produced by PDI will be investigated using hybrid simulations, to estimate what fraction of the Alfven wave power can be dissipated in the lower atmosphere.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12567", "attributes": { "award_id": "2229976", "title": "Collaborative Research: CyberTraining: Pilot: Operationalizing AI/Machine Learning for Cybersecurity Training", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28494, "first_name": "Jiawei", "last_name": "Yuan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2116, "ror": "", "name": "University of Massachusetts, Dartmouth", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The interplay between AI and cybersecurity introduces new opportunities and challenges in the cybersecurity of AI as well as AI for cybersecurity. However, operations and configurations of AI cyberinfrastructure with a security mindset are rarely covered in the typical curriculum. To fill this gap, this project intends to develop hands-on training materials and provide mentored training for trainees in engineering and science-related disciplines. By transforming and integrating training materials into the curriculum, this project benefits the potential cyberinfrastructure professionals in the community at large. This project has the potential to develop the research workforce in operating AI cyberinfrastructure with a security mindset to meet national and economic priorities. This project’s goal is broadening adoption of advanced cyberinfrastructure. This project develops a holistic technical approach for cybertraining: to identify, apply, and evaluate AI techniques which are inextricably related to well-defined operational cybersecurity challenges. The project intends to develop a Docker-based training platform that simulates and pre-configures a variety of scenarios to support hands-on AI cyberinfrastructure operations in the context of cybersecurity. Three levels of projects (exploratory, core, and advanced) are designed and integrated into the platform to help researchers and educators customize and develop for different education and training environments. The project broadens the access and adoption of advanced AI cyberinfrastructure while integrating cyberinfrastructure skills with the security mindset to foster inter-disciplinary and inter-institutional research collaborations. In addition to dissemination through publications and social media, the outcomes from this project have the potential to benefit the greater cyberinfrastructure community and beyond. This project is jointly funded by OAC and the CyberCorps program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12568", "attributes": { "award_id": "2306184", "title": "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-12-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28495, "first_name": "Tong", "last_name": "Shu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 253, "ror": "https://ror.org/00v97ad02", "name": "University of North Texas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "With the recent advancements in artificial intelligence, deep learning systems and applications have become a driving force in multiple transdisciplinary domains. While this evolution has been largely supported by the rapid improvements in advanced GPU cyberinfrastructure, comprehensive training materials are generally absent that combine application-driven deep learning techniques with the implementation of such techniques using the GPU cyberinfrastructure. To fill in this gap, this project develops an online workshop that comprises of a set of interdisciplinary cutting-edge training sessions offered by six faculty members from five disciplines. With a focus on the latest innovations in GPU-based deep learning systems and applications, this workshop fosters a community of the next-generation cyberinfrastructure users and contributors, who can use, develop, and improve advanced GPU cyberinfrastructure for their deep learning research. Such training efforts enhance the knowledge of the deep learning and GPU cyberinfrastructure workforce, and subsequently contribute to the solutions of important scientific and societal problems, including hydrographic mapping in geography, space environment nowcasting in aerospace, and autonomous driving and traffic monitoring in transportation. The workshop will also attract trainees from underrepresented groups, including minority students and researchers from rural areas.The interdisciplinary workshop developed in this project aims at enabling participants, including undergraduate seniors, graduate students, and researchers, to improve their multidisciplinary skill-sets, extend their academic research portfolios, develop their remote collaboration capacities, and significantly strengthen their career competitiveness. To achieve this goal, the intensive workshop includes 1) a set of hands-on lecture modules that provide trainees with comprehensive knowledge and skills on the full stack of deep learning systems in advanced GPU cyberinfrastructure, 2) a series of talks on the cutting-edge research in advanced GPU cyberinfrastructure and deep learning systems and application given by renowned scientists invited from academic and industrial research institutes, and 3) a remote open-ended interdisciplinary collaborative project of applying techniques introduced in lectures into practice. In addition, a prototype of an interactive online training system is developed to provide computing resources for the trainees and to track their learning progress, for more effective and efficient training activities. The project is expected to develop a future research workforce in deep learning systems and applications and to broaden the adoption of advanced GPU cyberinfrastructure in research and education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12569", "attributes": { "award_id": "2150143", "title": "REU Site: Accountability, Behavior, and Conflict in Democratic Politics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "RSCH EXPER FOR UNDERGRAD SITES" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-12-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28496, "first_name": "Sharece", "last_name": "Thrower", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the Directorate for Social, Behavioral and Economic Sciences. It has both scientific and societal benefits, and integrates research and education. This 8-week summer program provides mentored research experiences to a diverse group of 10 students per year, who engage in a set of activities designed to build skills and produce research on pressing socio-political questions. A key societal benefit is the production of research that generates new insights into the peaceful conditions, behaviors, and edifices that permit societies to flourish. This objective carries positive implications for national security: key global challenges are declining physical security and growing deficiencies in institutions that deliver effective policies and protect basic freedoms. To meet these challenges, we need to equip researchers with cutting-edge capabilities and we need to produce policy-relevant scientific research. A critical contribution to education is to build students’ interest and skills in the development, interpretation, and communication of rigorous social science research. An important contribution to national welfare is to prepare and support young citizens in the pursuit of careers involving the production, presentation, and consumption of rigorous social science research, especially in ways that advance strong societies and institutions. In addition, the program contributes to building a stronger research community by engaging and creating networks for scholars from minority-serving institutions, teaching institutions, and larger research institutions.REU participants are guided through two complementary experiences: a mentored independent research project and collaboration on a faculty affiliate’s project. Students attend skill-building sessions on research design, scientific inference, data analysis, professionalization, research communication, faculty research presentations, and career panels. The site engages collaborating partners at minority-serving institutions universities, local colleges, and professional organizations to amplify awareness of the program and recruit a wide range of participants. The site will generate new discoveries on how accountability, behavior, and conflict shape democratic politics. Topics include contentious politics, governance failings and their consequences, political polarization, and responsiveness in government institutions. The REU site will (1) provide mentored research opportunities to a diverse group of students; (2) train faculty and student mentors; (3) equip students with research skills, networks, and other resources for career development; (4) elevate the visibility and voices of individuals from under-represented groups; (5) connect with and include faculty from minority-serving institutions and a diverse network of researchers; and (6) provide students with the tools to effectively communicate research findings to academic and non-academic audiences. The site will provide mentored research experiences on topics related to accountability, behavior, and conflict in democratic politics; build interest, skillsets, and knowledge at the cutting edge of social science research; and prepare and support students entering career paths that involve the production, presentation, and informed consumption of scientific research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1383, "pages": 1424, "count": 14236 } } }