Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1394&sort=-keywords
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The combination of contentious politics and an energized electorate can result in record turnout despite a raging pandemic. The PIs examine how these features of the American polity shape public and institutional political behaviors. The project aims to build a network, and supportive infrastructure, to better understand how political elites, organizations, and movements in key political locations work to drive participation, preferences, and policymaking. \n\nThe project examines two broad research questions. The first question is: How do organizations and social movements mediate political preferences and policy agendas amongst the mass public? Second, it is interested in the collaboration between organizations and social movements and how these interactions shape traditional and untraditional forms of political participation. The study draws on a comprehensive mixture of quantitative (surveys, survey experiments, voter data analysis, social media analysis, and social network analysis) and qualitative (ethnographic observations, content analysis, elite interviews, and focus groups) methodological approaches to answer these questions. This study examines political activities during two electoral periods in several transformative states and municipalities. The broader impacts of the study are numerous. First, it connects a network of scholars from a diverse set of institutions. The project builds critical infrastructure at partner institutions to facilitate data collection and analysis. Namely, it (1) builds mobile research labs designed to conduct rapid response surveys during protests and organizational rallies, and (2) establishes data analysis centers at two minority serving institutions, and (3) provides cutting-edge training, tools, and professional resources to students from marginalized and underserved groups.\n\nThis 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": "10190", "attributes": { "award_id": "2132843", "title": "Collaborative Research: CIF: Small: A New Paradigm for Distributed Information Processing, Simulation and Inference in Networks: The Promise of Law of Small Numbers", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Comm & Information Foundations" ], "program_reference_codes": [], "program_officials": [ { "id": 1867, "first_name": "Phillip", "last_name": "Regalia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2022-09-30", "award_amount": 249962, "principal_investigator": { "id": 25944, "first_name": "Farhad", "last_name": "Shirani Chaharsooghi", "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": "The ability to access, process, and store distributed data in a reliable, efficient, and secure manner has become indispensable in everyday lives. A variety of emerging applications such as augmented reality, autonomous vehicles, and cloud computing heavily rely on handling large amounts of distributed information. The pandemic has further accentuated the global need for technologies that enable communication, collaboration, education and scientific discourse whilst maintaining physical distance, and this has increased awareness of the critical nature of the communication network infrastructure. The exponentially increasing demands for faster data processing and higher communication rates pose new challenges. This project addresses these challenges by developing novel approaches and techniques for distributed information processing, randomness generation, data storage and transmission, and inference. The project will tightly integrate research with a significant education and outreach program consisting of two focus areas: (i) Training students in interdisciplinary research, and (ii) Broadly disseminating research outcomes in the forms of new curricular development and student involvement. A concerted effort will be made to broaden the participation of women and under-represented minority students in the project. \n\nThe project is based on two research thrusts that are expected to provide a deeper understanding of the fundamental laws that govern the processing of information. In the first thrust, a new framework is developed based on two conceptual innovations: (i) A characterization of the fundamental memory structure of information processing functions using a novel notion of dependency spectrum, and (ii) Development of a new law of small numbers, which describes a fundamental interplay between the dependency spectrum and distributed cooperation. In particular, the project uncovers a trade-off between the correlation-preserving ability of distributed information-processing functions --- which is necessary for distributed cooperation --- and their ability to efficiently perform individual information-processing tasks. The second thrust addresses two application scenarios. (i) Building upon the concept of dependency spectrum, novel techniques are developed for distributed data compression, and transmission of information in interference and broadcast networks. (ii) The fundamental limits and practical design of distributed randomness generation algorithms are derived. These innovations lead to significant improvements over the state of the art both in terms of characterizations of asymptotic performance limits and constructive practical algorithms.\n\nThis 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": "10191", "attributes": { "award_id": "2122130", "title": "EAGER: SAI: Developing Effective and Culturally Appropriate Alaskan Housing: Performance metrics for future builds based on an interdisciplinary ethnography of past projects", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Strengthening American Infras." ], "program_reference_codes": [], "program_officials": [ { "id": 2516, "first_name": "Steven", "last_name": "Breckler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2023-08-31", "award_amount": 300000, "principal_investigator": { "id": 26137, "first_name": "Lisa", "last_name": "McNair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1648, "first_name": "Stacey A", "last_name": "Fritz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1649, "first_name": "Todd E", "last_name": "Nicewonger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26136, "first_name": "Frederick E", "last_name": "Paige", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.\n\nThis SAI EAGER award supports an interdisciplinary team of anthropologists, educators, builders, and engineers investigating the successes and failures of past housing projects in remote Alaskan communities. They are working with local research assistants to combine building diagnostics, local insights, socio-economic data, and culturally specific housing design. The team is working towards the creation of a repository of designs and findings that is available on an open-source platform. The data produced from this study will inform and strengthen future Alaskan infrastructure investments, and the research methods will lay the groundwork for similar research investigations in dozens of communities. The project broadens participation in engineering through collaborative research activities, makerspace activities, and community engagement.\n\nThe housing security crisis in rural Alaska, exacerbated by climate change and highlighted by the recent pandemic, places immense burdens on resource-strapped communities. While large-scale investments to address these problems may be on the horizon, there is a clear need for cutting-edge research and socially rich data on rural Alaskan housing to guide future projects and avoid mistakes of the past. This research project tackles this knowledge deficit with an experimental collaboration of experts and community members from inside and outside Alaska who are developing integrated techniques and ethnographically informed understandings of the infrastructural impacts that recent cold-climate demonstration homes have on the lived experiences of Alaskans. The research team is investigating the successes and failures of cold-climate demonstration homes in two distinct eco-regions (inland Brooks Range and coastal Yukon-Kuskokwim Delta). They are integrating ethnographic data with building diagnostics using human factors and engineering methods to assess the performance of construction practices. Data will be shared and co-analyzed during online participatory design workshops involving experts and community stakeholders who are invested in rural Alaska housing security issues. The community approach will reveal experiential knowledge of housing affordances, burdens, and expenses, and will result in a collection of post-design data related to housing security in rural Alaska. Data is also being used to develop performance metrics and guidance for future building projects in formats that meet the needs of communities or agencies. Taken together, these materials are providing content for an eventual design repository. Finally, the integrative methodology developed in this project lays the foundation for longer term research examining additional post-design sites. This project’s focus on the effects of the built environment on communities and the generalizability of the methods from the case studies will be replicable not just in other regions of Alaska, but in other appropriate regions of the U.S.\n\nThis 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": "10192", "attributes": { "award_id": "2142868", "title": "Strengthening the Industry 4.0 Workforce through Virtual Reality Training Modules", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [ { "id": 2366, "first_name": "Mary", "last_name": "Crowe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 593464, "principal_investigator": { "id": 26091, "first_name": "Jason", "last_name": "Simon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1327, "ror": "", "name": "Kentucky Community & Technical College System", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "As manufacturing contributes to higher export potential, better standards of living, and more jobs in America, addressing the need for a skilled technical workforce is crucial to support the future economic prosperity of the country. Moreover, as a result of the multiplier effect, manufacturing also impacts the broad economy, with every 100 jobs in a manufacturing facility creating 250 jobs in other sectors. In 2018, Deloitte and the Manufacturing Institute conducted a study on the manufacturing skills gap that revealed artificial intelligence, advanced robotics, automation, analytics, and the Internet of Things are emerging to transform the world of work (Industry 4.0) and are likely to create even more jobs than they replace. Since mid-2017, job openings in manufacturing have grown at double-digit rates, with a growing gap between open jobs and an available skilled talent pool to fill them. To help bridge this gap, this project of the Advanced Manufacturing Technical Education Collaborative (https://amtecworkforce.org/) will create a virtual reality (VR) application built on the zSpace platform that students can use to troubleshoot ten scenarios on an industry simulator. These scenarios will be integrated into a credit-bearing post-secondary capstone course for manufacturing students developed and designed with input from industry and education subject matter experts. \n\nThis partnership between representatives of industry and advanced technological education will ensure that the project strengthens the competency and global competitiveness of the advanced manufacturing workforce. The VR application and curricula will be field tested at targeted Advanced Manufacturing Technical Education Collaborative partner sites across the country, as well as with students and instructors currently using zSpace’s virtual reality platform. In addition, an exploratory curriculum will be developed to engage K-12 students in gaming-like simulations to recruit youth into advanced manufacturing technical training. Several objectives will guide the execution of the project. First is to engage secondary students in Industry 4.0 advanced manufacturing concepts through field-testing a newly created virtual reality “game-like” application. Second is to train post-secondary students preparing to be manufacturing technicians to enter the workforce with a basic understanding of Industry 4.0 technologies and the ability to apply them successfully in the workplace setting. Third is to increase the aptitude of secondary and post-secondary faculty in Industry 4.0 concepts and the use of virtual reality technology by providing comprehensive professional development. Three primary contributions to the field are anticipated. One is an expanded set of partnerships between academia, industry, and others to develop technician training that aligns with the growing Industry 4.0 infrastructure. Two is the improvement of secondary and post-secondary student learning in emerging Industry 4.0 technologies and virtual reality applications. Third is the lessening of disruptions of manufacturing technician training during a pandemic or similar event that creates a need for remote learning. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy. As a result, the project has the potential to contribute to improving the national STEM workforce.\n\nThis 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": "10193", "attributes": { "award_id": "2121121", "title": "Sparse Sensing, Actuation, and Communication in Complex Networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EPCN-Energy-Power-Ctrl-Netwrks" ], "program_reference_codes": [], "program_officials": [ { "id": 718, "first_name": "Donald", "last_name": "Wunsch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2024-08-31", "award_amount": 299987, "principal_investigator": { "id": 682, "first_name": "Milad", "last_name": "Siami", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Social networks that connect people, smart power grids, pandemic influenza mitigation, and flying drone display are all examples of large-scale complex networks. Due to the various worldwide applications of these networks, achieving efficient and robust control and monitoring of such networks is crucial, especially using limited resources. We seek to accelerate the development of novel technologies and advanced algorithms for complex dynamical networks using recent advances in theoretical computer science, graph theory, and machine learning. The results of this project will provide a rich set of building blocks to find a sparse yet important subset of available sensors and actuators in the complex network to enable precise control and high-resolution monitoring of the entire network. As an integral part of this research program, we also propose an educational plan involving K-12, undergraduate, and graduate-level education. The outreach element will improve the pre-college students' awareness of the impact and attractiveness of both research and engineering careers. Moreover, we aim to leverage the resources at Northeastern's University Program in Multicultural Engineering to broaden students' professional skills, find valuable contacts, and explore one or more career paths. We also expect to collect the results of this research in a special topic graduate-level course documenting the theoretical underpinnings and algorithmic implementations of sparse interaction in complex networks.\n\nGiven the increasingly large-scale nature of complex networks, it is crucial to rapidly estimate and control the state of the overall network in a distributed fashion with provable performance guarantees, while using a minimum amount of actuations, observations, and communications at each node and link over time. The main goal of this proposal is to generalize collaborative subset selection methods, and adaptive samplings in machine learning to (i) reduce network complexity and data overload by sparse scheduling of available Sensor/Actuator/Coupling measurements in complex networks, (ii) deal with structured uncertainties, missing information, and corruptions on the update dynamics of each agent or communication link, and (iii) handle a large class of controllability and observability performance measures that are not supermodular or submodular.\n\nThis 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": "10194", "attributes": { "award_id": "2127912", "title": "Collaborative Research: The Role of Elites, Organizations, and Movements in Reshaping Politics and Policymaking", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Build and Broaden" ], "program_reference_codes": [], "program_officials": [ { "id": 1532, "first_name": "Lee", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2025-05-31", "award_amount": 196240, "principal_investigator": { "id": 26138, "first_name": "Niambi", "last_name": "Carter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 288, "ror": "https://ror.org/05gt1vc06", "name": "Howard University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "Arguably, the current political climate is the function of three seemingly distinct, yet interrelated, ongoing phenomena: (1) a contentious, problem-laden political environment, (2) grassroots organizations driving unprecedented levels of engagement and turnout, and (3) national movements driving discourse, preferences, and reform around long-held policy grievances. The combination of contentious politics and an energized electorate can result in record turnout despite a raging pandemic. The PIs examine how these features of the American polity shape public and institutional political behaviors. The project aims to build a network, and supportive infrastructure, to better understand how political elites, organizations, and movements in key political locations work to drive participation, preferences, and policymaking. \n\nThe project examines two broad research questions. The first question is: How do organizations and social movements mediate political preferences and policy agendas amongst the mass public? Second, it is interested in the collaboration between organizations and social movements and how these interactions shape traditional and untraditional forms of political participation. The study draws on a comprehensive mixture of quantitative (surveys, survey experiments, voter data analysis, social media analysis, and social network analysis) and qualitative (ethnographic observations, content analysis, elite interviews, and focus groups) methodological approaches to answer these questions. This study examines political activities during two electoral periods in several transformative states and municipalities. The broader impacts of the study are numerous. First, it connects a network of scholars from a diverse set of institutions. The project builds critical infrastructure at partner institutions to facilitate data collection and analysis. Namely, it (1) builds mobile research labs designed to conduct rapid response surveys during protests and organizational rallies, and (2) establishes data analysis centers at two minority serving institutions, and (3) provides cutting-edge training, tools, and professional resources to students from marginalized and underserved groups.\n\nThis 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": "10195", "attributes": { "award_id": "2127910", "title": "Collaborative Research: The Role of Elites, Organizations, and Movements in Reshaping Politics and Policymaking", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "AIB-Acctble Institutions&Behav" ], "program_reference_codes": [], "program_officials": [ { "id": 1532, "first_name": "Lee", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2025-05-31", "award_amount": 229860, "principal_investigator": { "id": 26139, "first_name": "Najja", "last_name": "Baptist", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 586, "ror": "", "name": "University of Arkansas", "address": "", "city": "", "state": "AR", "zip": "", "country": "United States", "approved": true }, "abstract": "Arguably, the current political climate is the function of three seemingly distinct, yet interrelated, ongoing phenomena: (1) a contentious, problem-laden political environment, (2) grassroots organizations driving unprecedented levels of engagement and turnout, and (3) national movements driving discourse, preferences, and reform around long-held policy grievances. The combination of contentious politics and an energized electorate can result in record turnout despite a raging pandemic. The PIs examine how these features of the American polity shape public and institutional political behaviors. The project aims to build a network, and supportive infrastructure, to better understand how political elites, organizations, and movements in key political locations work to drive participation, preferences, and policymaking. \n\nThe project examines two broad research questions. The first question is: How do organizations and social movements mediate political preferences and policy agendas amongst the mass public? Second, it is interested in the collaboration between organizations and social movements and how these interactions shape traditional and untraditional forms of political participation. The study draws on a comprehensive mixture of quantitative (surveys, survey experiments, voter data analysis, social media analysis, and social network analysis) and qualitative (ethnographic observations, content analysis, elite interviews, and focus groups) methodological approaches to answer these questions. This study examines political activities during two electoral periods in several transformative states and municipalities. The broader impacts of the study are numerous. First, it connects a network of scholars from a diverse set of institutions. The project builds critical infrastructure at partner institutions to facilitate data collection and analysis. Namely, it (1) builds mobile research labs designed to conduct rapid response surveys during protests and organizational rallies, and (2) establishes data analysis centers at two minority serving institutions, and (3) provides cutting-edge training, tools, and professional resources to students from marginalized and underserved groups.\n\nThis 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": "10196", "attributes": { "award_id": "2118061", "title": "Collaborative Research: CyberTraining: Implementation: Medium: Cyber Training on Materials Genome Innovation for Computational Software (CyberMAGICS)", "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": [ { "id": 26118, "first_name": "Ashok", "last_name": "Srinivasan", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 705, "ror": "https://ror.org/002w4zy91", "name": "University of West Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2021-09-01", "end_date": "2025-08-31", "award_amount": 600000, "principal_investigator": { "id": 26142, "first_name": "Aiichiro", "last_name": "Nakano", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26140, "first_name": "Priya", "last_name": "Vashishta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26141, "first_name": "Ken-ichi", "last_name": "Nomura", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 152, "ror": "https://ror.org/03taz7m60", "name": "University of Southern California", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The computing landscape is evolving rapidly. Exascale computers can perform unprecedented mathematical operations per second, while quantum computers have surpassed the computing power of the fastest supercomputers. Concomitantly, artificial intelligence (AI) is transforming every aspect of science and engineering. To address these rapid changes and challenges, this project will train a new generation of materials cyberworkforce, who will solve challenging materials genome problems through innovative use of advanced cyberinfrastructure (CI) at the exa-quantum/AI nexus. Further, the project will foster the adoption of exa-quantum/AI nexus technologies by a broad research community and beyond through a unique dual-degree PhD/MS program, undergraduate research to close the research-education gap, and broadening participation of women and underrepresented groups.\n\nThis project will develop training modules for a new generation quantum materials simulator named AIQ-XMaS (AI and quantum-computing enabled exascale materials simulator), which integrates exa-scalable quantum, reactive and neural-network molecular dynamics simulations with unique AI and quantum-computing capabilities to study a wide range of materials and devices of high societal impact such as optoelectronics and pandemic preparedness. CyberMAGICS (cyber training on materials genome innovation for computational software) portal will be developed as a single-entry access point to all training modules that include step-by-step instructions in Jupyter notebooks and associated tutorial slides/videos, while providing online cloud service for those who do not have access to computing platform. The modules will be incorporated into the open-source AIQ-XMaS software suite as tutorial examples, and they will be piloted in classroom and workshop settings to directly train 1,200 CI users at the University of Southern California (USC) and Howard University, with a strong focus on underrepresented groups. Broader reach and training will be accomplished through the portal and nanoHUB. Students trained in the dual-degree program will earn a PhD in materials science or physics; they will also earn either an MS in computer science specialized in high-performance computing and simulations, MS in quantum information science, or MS in materials engineering with machine learning. Undergraduate students will be mentored and trained by academic scholars in multidisciplinary fields as well as by scientists at national labs and industry. The project will further broaden participation through USC’s Women in Science and Engineering (WiSE) program and undergraduate research by underrepresented groups jointly supervised by USC and Howard faculty.\n\nThis 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": "10197", "attributes": { "award_id": "2117282", "title": "MRI: Acquisition of a High-Performance Computer System to Support Research and Training in Computational Biology and Data Science at Meharry Medical College", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Major Research Instrumentation" ], "program_reference_codes": [], "program_officials": [ { "id": 1343, "first_name": "Marilyn", "last_name": "McClure", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 671411, "principal_investigator": { "id": 26144, "first_name": "Aize", "last_name": "Cao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26143, "first_name": "Qingguo", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 938, "ror": "https://ror.org/00k63dq23", "name": "Meharry Medical College", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "This MRI aims to acquire a High-Performance Computing (HPC) System to support research and training in Computational Biology and Data Science in a Historically Black College and University (HBCU). The system will allow Meharry Medical College to: \n• Support and expand multidisciplinary research in big data and areas with high Computational needs; support Maharry’s planned school of Applied Computational Sciences by incorporating state-of-the-art \ngenetics studies, learning, data visualization, and other functions requiring high performance computing, and other functions, and other areas with high computational needs, \n• Assist in recruiting and retaining historically underrepresented minority students in computational sciences and biology, \n• Expand educational and research outreach in High Performance Computing in Middle Tennessee and other HBCU. \nThis HPC system aims at providing robust computational processing to support programs at the Data Science Institute. \n\nThis system will provide significant computational support for high throughput research and advance knowledge in computational biology and data science. This includes, but is not limited to big data management, sequencing data analyses, image processing, machine learning, and other types of data analyses (sequencing data and Acade analyses). The system offers a safe place for resource sharing, and contributes in retaining valuable knowledge, as well as providing opportunities for collaboration and engaging in trying to restrain the pandemic, and attaining more students.\n\nThis 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": "10198", "attributes": { "award_id": "2044502", "title": "I-Corps Hub: Mid-Atlantic Region", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps Hubs" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2026-12-31", "award_amount": 15000000, "principal_investigator": { "id": 26146, "first_name": "Dean", "last_name": "Chang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26145, "first_name": "Darryll J", "last_name": "Pines", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps Hubs project is to catalyze an inclusive Mid-Atlantic innovation network to spur the development of deep technology startups that will have much-needed economic and societal impacts regionally, nationally, and globally. Research universities are positioned to support commercialization of scientific discoveries, and NSF I-Corps accelerates the growth and impact of university-launched deep technology ventures. The uniquely-experienced Mid-Atlantic Region I-Corps Hub team will build on the initial success of I-Corps Nodes to continue the cultural transformation at several of the country's top research universities to embed innovative practices into the conceptualization and conduct of research; to solve critical societal problems; and to create jobs, opportunities, and economic value. This Hub project directly aligns with the goals of the American Innovation and Competitiveness Act, which is critically important for previously industrialized regions such as the Mid-Atlantic region as it is experiencing significant economic turmoil due to shifts in American manufacturing industries, compounded by disruptions due to the pandemic. Also, the Hub vision and strategy prioritizes diversity, equity, and inclusion, particularly among underrepresented groups and underserved minorities and minority institutions like Historically Black Colleges and Universities (HBCUs).\n\nThe I-Corps program curriculum is built on scientific methods that incorporate hypothesis testing, the build-measure-learn cycle, and continuous iteration. Multiple outcomes result from the program, teams: change research directions, get funding from grants or private investors, or form start-up companies and grow through revenue. Equally important, the impact of the program accrues over many years. The Hub model enables alignment of a systematic and comprehensive analysis of regional impacts, as well as the programmatic, environmental, and individual factors that mediate the efficacy of the program itself. Understanding for whom and under what conditions I-Corps programs are most effective, and what factors contribute to or detract from this effectiveness is critical to inform the continued growth and scaling of I-Corps.\n\nThis 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": 1394, "pages": 1397, "count": 13961 } } }