Grant List
Represents Grant table in the DB
GET /v1/grants?sort=end_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=end_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=end_date", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=end_date", "prev": null }, "data": [ { "type": "Grant", "id": "13056", "attributes": { "award_id": "2121416", "title": "Development and Evaluation of a Comprehensive Utility Value Intervention for General Chemistry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 8035, "first_name": "Dawn", "last_name": "Rickey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-15", "end_date": null, "award_amount": 278389, "principal_investigator": { "id": 29061, "first_name": "Scott", "last_name": "Lewis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 235, "ror": "https://ror.org/032db5x82", "name": "University of South Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Many students who start college intending to major in a science, technology, engineering, or mathematics (STEM) field ultimately do not complete a degree in a STEM field. Addressing the issue of students leaving STEM majors is important because there may soon be a shortage of STEM professionals to meet the Nation’s demands. Prior research shows that some students experience a loss of motivation to pursue a STEM major particularly while taking introductory STEM courses. Learning theory suggests that motivation may be enhanced when a student sees the value of engaging with an activity due to its relevance for their future plans, referred to as its utility value. This Improving Undergraduate STEM Education (IUSE: EHR) Engaged Student Learning Level 1 project aims to serve the national interest by developing, implementing, and evaluating a comprehensive utility value intervention (UVI) for general chemistry courses. The UVI will be designed to improve students’ perceptions of the usefulness of general chemistry coursework, which is required for many STEM majors, and thus enhance students’ motivation to persist in general chemistry and STEM majors. <br/><br/>The hypothesized mechanism underlying the design of the UVI is informed by Eccles’ Expectancy Value Theory, which explains an individual’s engagement as the result of their expectations for success and subjective task value, including utility value. An expectation based on this theory is that improving students’ utility value of general chemistry will enhance students’ persistence and result in improved academic performance, as well as motivation to engage in similar courses in the future. The project team will implement the UVI in three course sections of first-semester General Chemistry and three course sections of second-semester General Chemistry at the University of South Florida. The UVI will include prompts for each student to relate general chemistry topics to their future plans, provide individualized feedback that incorporates links to chemistry-related news articles tailored to the plans each student identifies, and brief presentations on the applicability of general chemistry topics to STEM careers and everyday life applications. The intervention design will also be informed by interviews with senior undergraduate chemistry students and STEM professionals regarding the utility value of the general chemistry. The UVI will be evaluated using a quasi-experimental design that will examine intervention and comparison group students’ pre- and post-course perceived utility and course performance, as well as their STEM course enrollments one-year after the intervention. In addition, the team will conduct focus groups to provide a qualitative description of the impact of the intervention on students’ perceived utility value of chemistry. The implementation of the UVI will directly impact over 1200 students, and the dissemination plan includes professional development and support for thirty additional general chemistry instructors to incorporate UVIs into their courses. The NSF IUSE: EHR program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.<br/><br/>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": "14080", "attributes": { "award_id": "2103145", "title": "PostDoctoral Research Fellowship", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Workforce (MSPRF) MathSciPDFel" ], "program_reference_codes": [], "program_officials": [ { "id": 2352, "first_name": "Stefaan De", "last_name": "Winter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 150000, "principal_investigator": { "id": 30601, "first_name": "Paula", "last_name": "Burkhardt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2407, "ror": "", "name": "Burkhardt, Paula Elisabeth", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is made as part of the FY 2021 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines, under the mentorship of a sponsoring scientist. <br/><br/>The title of the project for this fellowship to Paula Burkhardt-Guim is \"C^0 Riemannian metrics with synthetic lower scalar curvature bounds and Ricci flow.\" The host institution for the fellowship is New York University, and the sponsoring scientist is Bruce Kleiner.<br/><br/>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": "13312", "attributes": { "award_id": "2149848", "title": "Developing the thermodynamic solid solution models for Th, U, REE phosphates needed to identify the formation conditions of Th, U-depleted REE ores", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Petrology and Geochemistry" ], "program_reference_codes": [], "program_officials": [ { "id": 2399, "first_name": "Rachel", "last_name": "Teasdale", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 489, "ror": "", "name": "Chico State Enterprises", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2022-03-01", "end_date": null, "award_amount": 425742, "principal_investigator": { "id": 29391, "first_name": "Xiaofeng", "last_name": "Guo", "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": "Rare earth elements (REE) are critical for the future of the U.S. economy, renewable energy, and national security. New technologies enhancing environmental sustainability, defense capability, and consumer products have sharply increased the demand for the REE. However, the supply of REE in the U.S. relies mostly on their import from foreign sources. Furthermore, many of the domestic deposits suffer from high concentrations of thorium (Th) and uranium (U) that contaminate the environment during mining. To deal with this issue, it is necessary to discover the conditions needed for economic concentrations of the REE to form in nature in the absence of Th or U; this requires understanding how Th and U mix with the REE in minerals and how such mixing can be avoided. This project will explore how Th and U mix with the REE in minerals through a combination of experiments and geological modeling. These results will make it possible to predict the natural environments in which Th- and U-poor REE minerals form and thereby provide geologists with the information they need to develop strategies to explore for and locate deposits of REE that can be economically and safely mined. This project also aims to educate high school, undergraduate, and graduate students in geochemistry, and prepare them for careers as scientists. The integrated education plan is committed to holding summer geochemistry schools for high school students, promoting geochemical education to student through visits to national laboratories and virtual lectures, and engaging students in the experimental and modeling methods that are used in the research project.<br/><br/>The objective of this research proposal is to generate new knowledge enabling identification of the conditions under which Th and U-depleted REE phosphate ores can form. One of the major impediments to the recovery of REE from ores in the U.S. is the radioactivity generated during their processing and refining due to the presence of high concentrations of Th and U in the main REE ore minerals, monazite and xenotime. Thus, there is a strong need to find Th, U-depleted REE ores, which, in turn, depends on developing a better understanding of their formation conditions (e.g., temperature, pressure, oxygen fugacity, pH, etc.), particularly those for which the incorporation of Th and U into phosphate structures is minimal. Whereas the properties of the aqueous species of the REE, U, and Th are now reasonably well-known for the hydrothermal conditions of REE ore formation, there is almost no information on phosphate-based REE/U/Th solid solutions. A major challenge is to correctly account for thermodynamic non-ideality due to the mixing of Th and U with the REE, which is often incorrectly assumed to be ideal and thus can lead to inaccurate or false predictions. This project will establish accurate thermodynamic models describing the incorporation of Th and U in REE phosphates that are critically needed by geochemical modelers to predict the mobilization, fractionation, and deposition of REE/U/Th in hydrothermal systems. The knowledge obtained will enable the development of new exploration techniques permitting the identification and localization of Th- and U-depleted REE ores. The project also offers unique opportunities for students to receive the interdisciplinary education and training needed to become geochemists with broad mindsets and skillsets. This includes summer geochemistry schools for high school students and teachers to develop their interest in the geochemistry of the REE and other critical metals.<br/><br/>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": "14336", "attributes": { "award_id": "2125941", "title": "Significant improvements in the development and application of olivine-melt thermometry and hygrometry: new experiments and analytical approaches", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Petrology and Geochemistry" ], "program_reference_codes": [], "program_officials": [ { "id": 6473, "first_name": "Jennifer", "last_name": "Wade", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": null, "award_amount": 414716, "principal_investigator": { "id": 30928, "first_name": "Rebecca", "last_name": "Lange", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Basaltic volcanism throughout Earth history has profoundly shaped the evolution of Earth’s crust and atmosphere, as well as the evolutionary path of life itself. In this study, erupted basalts are used as windows into the underlying mantle from which they were formed. The goal is to develop a new thermometer/hygrometer that enables both the temperature and water content of basalts to be readily and accurately obtained. Its ease of use will help generate large data sets, which will allow questions to be addressed about global differences in basalts erupted from diverse tectonic settings and throughout Earth history. The new thermometer/hygrometer will be made available to all interested users as a downloadable excel file. Two UM graduate students from diverse, under-represented backgrounds will work on this project. They will gain a broad range of skills, from expertise in conducting high-temperature and high-pressure experiments, performing thermodynamic calculations, and applying their results to better constrain mantle conditions that lead to partial melting. In addition, undergraduates from the M-STEM program at the University of Michigan (UM) will be recruited to work on this project.<br/><br/><br/>The goal of this project is to continue the experimental calibration a new olivine-melt thermometer that is based on the partitioning of Ni between olivine and basaltic liquids (DNiol/liq), and to further test whether it is independent of dissolved water at both crustal and mantle depths. Currently, the most widely used olivine-melt thermometers (based on DMgol/liq) are strongly dependent on water, and their application to hydrous basalts requires that melt H2O concentrations already be known, which is not always the case. With an H2O-independent thermometer in hand, based on DNiol/liq, it can be applied to olivine phenocrysts in hydrous basalts to obtain accurate temperatures, without prior knowledge of melt water contents. These temperatures can then be combined with Mg-based olivine-melt thermometers to obtain anhydrous temperatures and thus the magnitude of ∆T, the depression of the olivine liquidus due to dissolved water. Experiments will be performed to evaluate whether this depression of the olivine liquidus due to water is linearly and inversely correlated with the partitioning of Ca between olivine and melt (DCaol/liq), as indicated in preliminary work. To extend the calibration of this new olivine-melt thermometer/hygrometer, experiments will be performed on a variety of basaltic compositions over a range of water contents, temperatures, pressures (crustal and mantle) and fO2 conditions. Additionally, it is proposed to apply the new thermometer/hygrometer to a large number of natural basalts from a variety of tectonic settings. The results will shed light on the thermal/hydrous conditions in the mantle during partial melting across a variety of tectonic settings, from subduction zones to mantle plumes to continental rifts.<br/><br/>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": "15104", "attributes": { "award_id": "2406129", "title": "SCH: AI-Enhanced Risk Assessment for Mitigating Indoor Viral Transmission in Public Schools", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "OE Operations Engineering" ], "program_reference_codes": [], "program_officials": [ { "id": 2155, "first_name": "Georgia-Ann", "last_name": "Klutke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 642608, "principal_investigator": { "id": 27368, "first_name": "Yu", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31650, "first_name": "Chenang", "last_name": "Liu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 387, "ror": "https://ror.org/01g9vbr38", "name": "Oklahoma State University", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true }, "abstract": "This Smart and Connected Health (SCH) award will support research that advances national health, prosperity, and welfare by investigating the potential of Heating, Ventilation, and Air Conditioning (HVAC) systems to mitigate the spread of airborne viruses such as SARS-CoV-2 and other pollutants in school classrooms. Due to the nature of the indoor classroom environment, school-aged children are particularly vulnerable to infectious diseases, and most current HVAC systems are not optimized to effectively prevent cross-infections. This research project combines a computational model that captures the effects of airflow on viral transport, uptake, and immune response with a generative Artificial Intelligence (AI) model trained by laboratory data and simulation experiments to improve design and real-time control of air handling technology. By optimizing HVAC systems to minimize infection risks, the project plans to contribute to healthier indoor environments, reducing the incidence of disease transmission and improving overall public health outcomes. <br/><br/>The goal of this research project is to develop a robust, multiscale computational model to understand the relationship between HVAC design, indoor airflow, virus emission, transmission, and infection risks among children in representative indoor environments. Specifically, the research objectives are to: (1) determine the spatiotemporal concentration distribution of pollutant- and virus-laden aerosols in classrooms with various layouts and children’s respiratory systems, using a model that combines Computational Fluid Dynamics (CFD) and Host Cell Dynamics (HCD) to generate infection risk indices that guide HVAC system design optimization; and (2) develop a generative AI-empowered tool for efficient HVAC design and real-time control to mitigate infection risks. The computational model aims to predict virus-laden aerosol transport, distribution, and infection risks from emission sites to children’s respiratory system under multiple HVAC configurations. The generative AI model plans to deploy generative adversarial networks (GAN) and diffusion models for the design and optimization of HVAC systems, reducing computational costs and enhancing design efficiency. This project leverages the interdisciplinary expertise of the research team to with the intent of creating a transformative tool for public health enhancement. The project includes outreach to engage K-12 students, educators, and the broader community, raising awareness about the importance of indoor air quality and the role of advanced technologies in public health. Additionally, the project provides interdisciplinary training opportunities for students and researchers in engineering, computer science, data science, and public health, promoting diversity and inclusion in these fields.<br/><br/>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": "13824", "attributes": { "award_id": "2111403", "title": "Collaborative Research: Integrating Perspective-taking and Systems Thinking for Complex Problem-Solving", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 1869, "first_name": "Thomas", "last_name": "Kim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-15", "end_date": null, "award_amount": 372888, "principal_investigator": { "id": 30167, "first_name": "Rebecca", "last_name": "Jordan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30167, "first_name": "Rebecca", "last_name": "Jordan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 521, "ror": "https://ror.org/05hs6h993", "name": "Michigan State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by using computer modeling to present multiple perspectives, helping students to improve systems thinking and complex socio-technical problem-solving. The topics with which STEM fields grapple are often not merely scientific problems; they are also at times ideologically and emotionally charged issues. Such contemporary “socio-scientific” issues affect multiple stakeholders in different ways and have real and diverse consequences, both economic and societal. Understanding and responding to these socio-scientific issues, such as genetically modified crops, vaccine development/deployment and climate change mitigation, requires that individuals not only understand scientific content and how systems work, but also how these systems look from different vantage points. The goal of this project is to develop and assess new teaching strategies to improve understanding and decision-making related to such complex social and environmental problems. This project will build on research from previous studies to develop the state-of-the-art undergraduate instruction strategies. The software, curricular tools, and case studies being designed will be used by college instructors across the United States to promote perspective-taking and problem-solving. Formally engaging students in this type of thinking is essential to the training of America’s future workforce. <br/><br/>Specifically, students will engage with a series of case studies on complex socio-scientific issues, and use the MentalModeler (www.mentalmodeler.org) software to map out the system from the perspectives of different stakeholders. We believe this approach will promote systems thinking, model-based reasoning, perspective taking, and problem-solving ability in the undergraduate classroom. We intend to test the hypothesis that integrating novel perspective taking and systems modeling across different undergraduate courses: 1) helps students overcome some of the cognitive and motivational obstacles elicited by controversial socio-scientific topics, 2) leads to a deeper and more accurate understanding of the system itself, and 3) helps learners create and support arguments regarding the effect of interventions on system-level outcomes. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.<br/><br/>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": "12800", "attributes": { "award_id": "2302813", "title": "Collaborative Research: Adaptable Game-based, Interactive Learning Environments for STEM Education (AGILE STEM)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Cyberlearn & Future Learn Tech" ], "program_reference_codes": [], "program_officials": [ { "id": 1414, "first_name": "Soo-Siang", "last_name": "Lim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-15", "end_date": null, "award_amount": 350000, "principal_investigator": { "id": 28719, "first_name": "Daniell", "last_name": "DiFrancesca", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 28719, "first_name": "Daniell", "last_name": "DiFrancesca", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Learners of all ages are expected to be prepared to interact with emerging and technology-driven work environments. In addition, the growing reliance on online learning and its unprecedented and unexpected acceleration due to the COVID-19 pandemic are expected to change the education landscape forever. Thus, there is a need to grow the development of digital platforms for teaching and learning. Emerging technologies such as machine learning and high fidelity simulated environments have the potential to create customized and adaptable learning environments to support STEM learning outcomes. This project serves the national interest by advancing the knowledge about designing and creating adaptable game-based, interactive learning environments for STEM. The inclusion of underrepresented minority and female learners in the design stages of these learning environments, their portability, as well as the capability of these environments to be customized and adaptive have the potential to enhance education equality, engagement, and learning outcomes, and broaden their usability to several STEM domains. Moreover, the narratives and simulation models are inspired by real-world systems. Therefore, the learning environments are expected to enhance the learner’s understanding of complex system concepts that are challenging to understand using traditional teaching approaches and will help build the much-needed skills for the U.S. future STEM workforce. The proposed emerging technologies do not necessarily need access to specialized equipment, which eliminates barriers to scalability and border implementation and use. <br/><br/>The primary goals of this project are to automatically customize and adapt three-dimensional (3D) simulated game-based learning environments to improve engagement, and provide a deeper understanding of their design, development, and deployment, impact on learning and self-regulated learning (SRL) skills, and knowledge transferability from the learning environments to real-life applications. The project addresses the lack of scientific evidence and/or work in the following thrust areas: 1) the potential of reducing the barriers to content generation of 3D simulated game-based learning environments using emerging and advanced machine-learning methods; 2) creating customized content and adaptive 3D simulated game-based learning environments that improve and maintain learners motivation and engagement, enhance learning via instructional assistive content scaffolding, and increase knowledge transferability from game to real-life applications; 3) assessing the effectiveness of the learning environments for all learner groups in online and residential settings; and 4) exploring how learner decision-making and behavior data in the simulated game-based learning environments, and eye-tracking, facial expressions, bio-signals, and usage data, enhance knowledge about the relationships between decision-making/usage and SRL skills development.<br/><br/>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": "12544", "attributes": { "award_id": "2229975", "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": 28473, "first_name": "Houbing", "last_name": "Song", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 315, "ror": "", "name": "Embry-Riddle Aeronautical University", "address": "", "city": "", "state": "FL", "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 (CI) with a security mindset are rarely covered in the typical AI curriculum. To fill this gap, this project intends to develop hands-on training materials and provide mentored training for current and future research workforce in engineering and science-related disciplines. By transforming and integrating training materials into a course curriculum, this project aims to train potential cyberinfrastructure professionals in the CI community at large to handle AI with and for cybersecurity. This project has the potential to develop the research workforce in operating AI cyberinfrastructure with a security mindset to meet the national and economical needs and priorities of CI advancement. This project’s goal is to broaden the adoption of advanced cyberinfrastructure through training. 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 into different education and training environments. The project democratizes 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 the dissemination through publications and social media, the outcomes from this project have the potential to benefit the greater cyberinfrastructure community and beyond, through the training and the sharing of the \"AI for and with cybersecurity\" course curriculum. 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": "13568", "attributes": { "award_id": "2144751", "title": "CAREER: Efficient, Dynamic, Robust, and On-Device Continual Deep Learning with Non-Volatile Memory based In-Memory Computing System", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software & Hardware Foundation" ], "program_reference_codes": [], "program_officials": [ { "id": 977, "first_name": "Sankar", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": null, "award_amount": 500000, "principal_investigator": { "id": 8714, "first_name": "Deliang", "last_name": "Fan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "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).<br/><br/>Over past decades, there have existed grand challenges in developing high performance and energy-efficient computing solutions for big-data processing. Meanwhile, owing to the boom in artificial intelligence (AI), especially Deep Neural Networks (DNNs), such big-data processing requires efficient, intelligent, fast, dynamic, robust, and on-device adaptive cognitive computing. However, those requirements are not sufficiently satisfied by existing computing solutions due to the well-known power wall in silicon-based semiconductor devices, the memory wall in traditional Von-Neuman computing architectures, and computation-/memory-intensive DNN computing algorithms. This project aims to foster a systematic breakthrough in developing AI-in-Memory computing systems, through collaboratively developing ahybrid in-memory computing (IMC) hardware platform integrating the benefits of emerging non-volatile resistive memory (RRAM) and Static Random Access Memory (SRAM) technologies, as well as incorporating IMC-aware deep-learning algorithm innovations. The overarching goal of this project is to design, implement, and experimentally validate a new hybrid in-memory computing system that is collaboratively optimized for energy efficiency, inference accuracy, spatiotemporal dynamics, robustness, and on-device learning, which will greatly advance AI-based big-data processing fields such as computer vision, autonomous driving, robotics, etc. The research will also be extended into an educational platform, providing a user-friendly learning framework, and will serve the educational objectives for K-12 students, undergraduate, graduate, and under-represented students.<br/><br/>This project will advance knowledge and produce scientific principles and tools for a new paradigm of AI-in-Memory computing featuring significant improvements in energy efficiency, speed, dynamics, robustness, and on-device learning capability. This cross-layer project spans from device, circuit, and architecture to DNN algorithm exploration. First, a hybrid RRAM-SRAM based in-memory computing chip will be designed, optimized, and fabricated. Second, based on this new computing platform, the on-device spatiotemporal dynamic neural network structure will be developed to provide an enhanced run-time computing profile (latency, resource allocation, working load, power budget, etc.), as well as improve the robustness of the system against hardware intrinsic and adversarial noise injection. Then, efficient on-device learning methodologies with the developed computing platform will be investigated. In the last thrust, an end-to-end DNN training, optimization, mapping, and evaluation CAD tool will be developed that integrates the developed hardware platform and algorithm innovations, for optimizing the software and hardware co-designs to achieve the user-defined multi-objectives in latency, energy efficiency, dynamics, accuracy, robustness, on-device adaption, etc.<br/><br/>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": "14848", "attributes": { "award_id": "2406488", "title": "Implementation Project: Leveraging Innovation and Discovery for STEM Success (LIDSS)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Hist Black Colleges and Univ" ], "program_reference_codes": [], "program_officials": [ { "id": 27246, "first_name": "Alfred", "last_name": "Hall", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-15", "end_date": null, "award_amount": 3000000, "principal_investigator": { "id": 31528, "first_name": "Connie", "last_name": "Walton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31527, "first_name": "Stacey", "last_name": "Duhon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2502, "ror": "https://ror.org/05mnb6484", "name": "Grambling State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Historically Black Colleges and Universities - Undergraduate Program (HBCU-UP) provides support to strengthen STEM undergraduate education and research at HBCUs. This Implementation Project: Leveraging Innovation and Discovery for STEM Success (LIDSS) is a comprehensive effort at Grambling State University to prepare highly competitive STEM graduates to meet the challenges of an ever-changing world. Discovery and Innovation are the core of the design for each strategic activity. This project aligns with the goals of HBCU-UP in its work to foster STEM student success via its support of faculty research experiences, student support programs, and outreach initiatives for K-12 students and teachers.<br/><br/>The overarching goal of this project is to enhance the ability of Grambling State University to train highly prepared STEM majors to meet workforce needs, while reversing the effects that the pandemic has had on education at all levels. The components of this project were identified using a challenge-based learning approach. STEM faculty and students identified the problems and provided possible solutions. The overall premise is STEM education must not remain static but constantly evolve to meet a changing world. The model used to design each component of this project has discovery and innovation as the core for STEM Learning. The LIDSS project aims to improve the recruitment, retention and graduation of STEM students. A priority will be given to the recruitment of veterans as STEM majors. A STEM Entrepreneurship Academy and a Makers Space will support faculty being able to integrate entrepreneurship within curricula to further nurture the creativity of STEM majors. A Student Success Initiative will be established that will create a judgement free zone where students can enhance skills with assistance from faculty/student leader teams. This project aims to establish partnerships with research intensive institutions to expand the research capacity of STEM faculty through collaboration and mentoring opportunities. The results of this project should be of great interest to educators who also face challenges related to recruiting, retaining and graduating STEM students who are prepared to be innovative leaders. A Biennial Symposium that will focus on the use of innovative educational practices to promote STEM learning will be hosted on campus. Data collected in this project, including the symposia, will advance the knowledge of best practices that will lead to improved STEM programs that are nimble and able to utilize innovative strategies to respond to ever changing needs.<br/><br/>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": 1, "pages": 1392, "count": 13920 } } }