Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-keywords
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Pandemics emerge through incidental ‘perfect storms’: molecular changes in pathogens, gradual trends in climate, subtle shifts in ecological interactions among potential hosts, and individual behavioral decisions by people, all colluding to make up the difference between an interesting but rare new variant of a known disease and an existential worldwide crisis. Being able to predict the emergence of pandemic threats, therefore, requires a fully integrated, multidisciplinary approach, able to consider the complexity of these realms across scales of interaction to predict and, ideally, prevent. This workshop is one of four bringing experts from scholarly communities in the social and behavioral sciences, biology, engineering, and computer science together to discuss how to integrate the approaches taken by each community into a more effective, unified science of pandemic prediction. The focus of this workshop is on developing understanding of how human attitudes, social behavior, and the drivers underlying both shape patterns of infectious-disease transmission and efforts at control and eradication. This fundamental understanding in turn will facilitate pandemic prevention and control decisions that leave us better prepared when confronted with future pandemic threats.\n\nThe workshop is structured to focus on four topical areas of crucial importance, dealing with cultural transmission, information and communication, equity, and sustainability. The white paper that comes from this workshop will provide important guidance for incorporating insights into the social and behavioral sciences into predictive intelligence and pandemic prevention. It will inform future research investments, institutional capacity-building, and other policy priorities aimed at keeping the US and the world safe from inevitable future pandemics.\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": "10127", "attributes": { "award_id": "2119117", "title": "Collaborative Research: Predictive Intelligence for Pandemic Prevention, Theme 4: Social and Behavioral Obstacles and Supports", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Sociology" ], "program_reference_codes": [], "program_officials": [ { "id": 1173, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2023-01-31", "award_amount": 29724, "principal_investigator": { "id": 14871, "first_name": "Juliet", "last_name": "Iwelunmor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 734, "ror": "https://ror.org/01p7jjy08", "name": "Saint Louis University", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Although pandemics have threatened human civilization since ancient times, how to predict and prevent them remains a pressing challenge, calling for innovative insights and practices. Pandemics emerge through incidental ‘perfect storms’: molecular changes in pathogens, gradual trends in climate, subtle shifts in ecological interactions among potential hosts, and individual behavioral decisions by people, all colluding to make up the difference between an interesting but rare new variant of a known disease and an existential worldwide crisis. Being able to predict the emergence of pandemic threats, therefore, requires a fully integrated, multidisciplinary approach, able to consider the complexity of these realms across scales of interaction to predict and, ideally, prevent. This workshop is one of four bringing experts from scholarly communities in the social and behavioral sciences, biology, engineering, and computer science together to discuss how to integrate the approaches taken by each community into a more effective, unified science of pandemic prediction. The focus of this workshop is on developing understanding of how human attitudes, social behavior, and the drivers underlying both shape patterns of infectious-disease transmission and efforts at control and eradication. This fundamental understanding in turn will facilitate pandemic prevention and control decisions that leave us better prepared when confronted with future pandemic threats.\n\nThe workshop is structured to focus on four topical areas of crucial importance, dealing with cultural transmission, information and communication, equity, and sustainability. The white paper that comes from this workshop will provide important guidance for incorporating insights into the social and behavioral sciences into predictive intelligence and pandemic prevention. It will inform future research investments, institutional capacity-building, and other policy priorities aimed at keeping the US and the world safe from inevitable future pandemics.\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": "10128", "attributes": { "award_id": "2055223", "title": "Mobile Controlled Environment Agriculture Technician Education", "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": "2021-07-01", "end_date": "2024-06-30", "award_amount": 300000, "principal_investigator": { "id": 26030, "first_name": "Lew", "last_name": "Nakamura", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26029, "first_name": "BERNARD R MICHELS", "last_name": "III", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 684, "ror": "", "name": "University of Hawaii", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true }, "abstract": "The Hawaiian Islands currently import 90% of its food. Consequently, it needs to expand local food production. This project aims to address this need by creating a mobile Controlled Environment Agriculture (CEA) greenhouse and use it to teach students CEA skills. The mobile greenhouse can be deployed in challenging environments (e.g., volcanic eruption, hurricane, pandemic), thus improving the resiliency of Hawaii’s isolated communities. In addition, this mobile CEA greenhouse will increase participation of native Hawaiian students underrepresented in STEM fields by linking to ongoing cultural and “aina” (land) values such as sustainability. The new CEA curriculum will engage students from agricultural and technology programs in problem-based learning. The hands-on environment, together with involvement of students from different disciplines, can enhance students’ critical thinking skills and the ability to work in multidisciplinary teams. Completion of the CEA curriculum, with its problem-based learning approach, has the potential improve employability of participating students across a range of jobs and industries. \n\nUsing a new insulated and refrigerated shipping container with a commercial trailer, Hawaii Community College will work with industry advisors to design and build the mobile CEA greenhouse and upgrade existing courses to include CEA knowledge and skills. The CEA curriculum will focus on building, automating, and optimizing the resources, operations, and yield from the technology-enabled mobile greenhouse. The greenhouse environment will provide a strong context for the new PBL pedagogy, which will involve students from different disciplines with the goal of increasing students’ skills and employability with a broad range of employers. The mobile green house will be brought to middle and high schools to enhance visibility of the technology, the varied employment opportunities available to those with training, and showcase the potential of integrating agriculture and technology programs in Hawaii. Results from the project’s annual evaluation reports will be used to adjust activities and measure success. These results will also inform stakeholders in education, agriculture, engineering, and the public. 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.\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": "10129", "attributes": { "award_id": "2040488", "title": "SCC-CIVIC-PG Track B: An Integrated Scenario-based Hurricane Evacuation Management Tool to Support Community Preparedness", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 1082, "first_name": "Sandip", "last_name": "Roy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2021-12-31", "award_amount": 50000, "principal_investigator": { "id": 26032, "first_name": "Rachel", "last_name": "Davidson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26031, "first_name": "Tricia", "last_name": "Wachtendorf", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 442, "ror": "https://ror.org/01sbq1a82", "name": "University of Delaware", "address": "", "city": "", "state": "DE", "zip": "", "country": "United States", "approved": true }, "abstract": "As a hurricane approaches, emergency managers must determine when and where to issue official evacuation orders. It requires integrating large amounts of uncertain, changing information to make consequential decisions in a short time frame under pressure, and the stakes are high. An opportunity exists to leverage recent research—in particular, the Integrated Scenario-based Evacuation (ISE) tool—to help meet that challenge. This team designed the ISE tool to be run for a particular hurricane as it approaches the U.S. When run at a point in time, it generates a set of contingency plans and defines the circumstances under which to implement each, depending on how the hurricane evolves. Each plan includes recommendations about whether or not to issue an evacuation order for each geographic evacuation zone, and if so, when.\n\nWhile the new technology has promise, moving from research to practice brings its own challenges. The objectives of Stage 1, therefore, are to: (1) Determine how the new tool and its output can support emergency managers’ natural decision-making process; (2) Conduct a needs assessment for the tool; and (3) Advance understanding of community innovation in disaster management. The Stage 2 objective is to implement an operational prototype of the ISE-based decision support tool for North Carolina. The emergency manager partners will ensure the tool is of practical use; the researchers will ensure it reflects the best science; and the industry partner will ensure its impact is sustainable by hosting it on their platform.\n\nThe ISE tool uses a multi-stage stochastic programming model to provide a tree of recommended evacuation orders and a performance evaluation for that set of recommendations. Benefits of the tool are that it provides an integrated hazard assessment with uncertainty that includes the effects of storm surge, wind waves, tides, river discharge, inland flooding, and wind; it explicitly balances competing objectives of minimizing risk and travel time; it offers a well-hedged solution robust under the range of hurricane evolutions; and it is adaptive, leveraging the value of decreasing uncertainty during an event. Stage 1 will include focus groups of key stakeholders to determine the process of innovation; a needs assessment; and analysis and planning for the next stage. In Stage 2, we will (1) make the tool faster and easier to run by moving it to a GPU platform and exploiting opportunities for parallelization; (2) develop an interactive graphical user interface; and (3) improve the modeling by adding treatment of institutionalized vulnerable populations.\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": "10130", "attributes": { "award_id": "2051440", "title": "REU Site: Research Experience for Undergraduates in Genomics and Biochemistry", "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": [ { "id": 1044, "first_name": "Sally", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2024-02-29", "award_amount": 328827, "principal_investigator": { "id": 11950, "first_name": "Fern", "last_name": "Tsien", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1160, "ror": "", "name": "Louisiana State University Health Sciences Center", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1160, "ror": "", "name": "Louisiana State University Health Sciences Center", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "This REU Site award to the Louisiana State University Health Sciences Center, located in New Orleans, LA, will support the training of 10 students interested in genomics and/or biochemistry research for 10 weeks during the summers of 2021-2023. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities or from groups underrepresented in the sciences, will be trained in the program. All projects are hypothesis-driven, focusing on solving scientific problems and utilizing computational state-of-the-art scientific methods. Students will analyze and interpret data and communicate their findings. They will develop a competitive resume, receive training in potential careers in STEM, and contribute new knowledge in genomics and biochemistry. Student projects will be flexible for remote and/or on-campus work. By the end of the program, students of diverse backgrounds will become more competitive in their graduate school applications and research careers. Students will learn how research is conducted, and many of them will present the results of their work at scientific conferences. \n\nThe program will focus on one-on-one training in genomics and biochemistry, emphasizing hard scientific skills (hypothesis and experimental design, methodology, scientific communication), soft skills (time management), and professional development (responsible conduct in research, resume/CV writing, career guidance). REU students will have networking opportunities with current graduate students, post-doctoral fellows, and faculty members. They will have the opportunity to enhance their scientific communication thru interaction with inner-city K-12 schools. The goals of this program are to facilitate training in data analytics and scientific visual literacy, to provide learning platforms for current datasets and research problems, and enable trainees to use these materials in a variety of situations (e.g., a pandemic). Computational and wet lab projects adaptable to remote applications will be available based on student research interests. Assessments of the program will be performed through the online SALG URSSA tool. Students will be tracked after the program in order to follow their career paths. More information about the program is available by visiting http://www.medschool.lsuhsc.edu/genetics/reu.aspx , or by contacting the PI (Dr. Fern Tsien) at [email protected] or [email protected].\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": "10131", "attributes": { "award_id": "2036161", "title": "SBIR Phase I: Development of a novel technology to manufacture animal-free muscle proteins in bacteria and yeasts", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2022-05-31", "award_amount": 256000, "principal_investigator": { "id": 26033, "first_name": "Chenfeng", "last_name": "Lu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1888, "ror": "", "name": "FYBRAWORKS FOODS INC.", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial impact of this Small Business Innovation Research (SBIR) Phase I project includes improved food security, environmental benefits, and human health benefits. This project will develop a meat alternative that more closely mimics the taste and texture of animal meat. The technology developed from the proposed project will be used to build a vertically integrated food manufacturing platform that can withstand supply chain disruptions from natural disasters and pandemics. These meat alternatives will reduce and replace traditional meat consumption, with many environmental benefits - lowered greenhouse gas emissions and aquatic pollution; lower use of energy, water and land; and reduced antibiotics usage. There are benefits to human health for reduced meat consumption. Furthermore, production costs will be eventually be comparable to that of mushroom farming.\n\nThe proposed project aims to develop a fermentation-based meat alternative that more closely mimics the taste and texture of animal meal through recombinant protein technologies. Additionally, the project aims to leverage the texture and flavor of mushroom mycelia, and supplement this with recombinant muscle proteins to further enhance the taste and nutritional profiles and overcome many of the shortcomings of existing plant-based meat products. To date the concept of combining recombinant muscle protein and single cell protein is novel and has not been reported. Large gaps persist between plant-based and lab-grown meat, with regards to cost and consumer experience. Plant-based meat is more affordable but faces consumer resistance due to sub-optimal texture and nutritional profiles, while cultivated meat offers a consumer experience similar to that of animal meat but at a much higher cost. Towards this goal, muscle protein genes will be expressed in a microbial host and enzymatically cross-linked with vegetable protein to produce protein fibers that can be formulated into synthetic meat. The technical objectives also include demonstration the feasibility of crosslinking muscle fiber proteins extracted from meat with mycoprotein from mushroom to produce desired textural and flavor properties.\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": "10132", "attributes": { "award_id": "2104223", "title": "I-Corps: A contactless, non-intrusive, artificial intelligence (AI)-enabled contact tracing system for reducing the spread of viruses", "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": [ { "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": "2021-02-01", "end_date": "2023-04-30", "award_amount": 50000, "principal_investigator": { "id": 26035, "first_name": "Mona", "last_name": "Azarbayjani", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26034, "first_name": "Hamed", "last_name": "Tabkhi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 248, "ror": "https://ror.org/04dawnj30", "name": "University of North Carolina at Charlotte", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is to use recent advances in artificial intelligence (AI) and deep learning to enhance public health challenges in nursing homes. Fever, as a non-specific measure of infection, is commonly observed in a broad range of diseases and pandemics. The proposed AI-powered assessment system will create an effective, non-intrusive tool for empowering nursing home facilities and clinics to combat the spread of contagious diseases and future pandemics, all the while providing a higher health resiliency for our communities. The proposed technology creates a real-time health surveillance system that also may be adopted and customized to a wide range of public health applications that require continuous, non-intrusive health monitoring with predictive analytics and proactive decision making. The proposed research has significant opportunities both in the public and private sectors.\n\nThis I-Corps project is based on the development of a monitoring system to mitigate the risk and control the spread of epidemic viruses through real-time artificial intelligence, multi-sensor fusion, and video data analytics. In contrast to existing approaches that have a narrow focus with limited intelligence capabilities, the proposed technology offers a holistic solution to enable scalable, reliable symptom assessment and contact tracing from a distance with strict personal privacy measures ensured. By utilizing both red green blue (RGB) and thermal cameras (off-the-shelf products), it may be possible to provide a more precise system that is capable of monitoring several health indicators simultaneously; e.g., body temperature, respiratory rate, coughing, and sneezing while taking a non-intrusive approach. The proposed device is equipped with an AI-enabled contact tracing system for reducing the spread of viruses by identifying the potentially infected individuals at the early stage. For privacy-aware contact tracing, the plan is to leverage previously developed technology for real-time privacy built-in human pose estimation, re-identification, trajectory analysis, and activity recognition. The technology creates lightweight, end-to-end execution of real-time computer vision based on RGB cameras, with the ability to perform at a high frame rate on embedded and edge devices.\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": "10133", "attributes": { "award_id": "2046102", "title": "CAREER: An Algorithm and System Co-Designed Framework for Graph Sampling and Random Walk on GPUs", "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": 2785, "first_name": "Almadena", "last_name": "Chtchelkanova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2026-02-28", "award_amount": 584001, "principal_investigator": { "id": 26036, "first_name": "Hang", "last_name": "Liu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1113, "ror": "https://ror.org/02z43xh36", "name": "Stevens Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Graph analytics is one of the key technologies to address the grand challenges of our time, such as understanding the spread of pandemics, designing extremely large-scale integrated circuits and uncovering software vulnerabilities among many others. However, as the size of the graph continues to grow, learning, mining and computing such gigantic graphs become ineffective, impractical, and potentially dire. Fortunately, Graph Sampling and Random Walk can dramatically reduce the size of the original graphs, while still capturing the desired properties for downstream graph analytics tasks. But a comprehensive system that can perform graph sampling and random walk on real-world trillion-edge graphs at an acceptable speed is absent. This research pioneers the effort of uniting various graph sampling and random walk algorithms behind a user-friendly framework that can take advantage of world-class Graphics Processing Unit (GPU) computing facilities, including the future exascale ones, to rapidly handle trillion-edge graphs. This project contributes to the U.S. national goal of increasing participation in science and engineering, which is crucial to America’s success in addressing global challenges, building a stronger and more diversified workforce, and meeting the needs of the global innovation economy. This project produces a high-performance software library that serves as a foundational tool for fellow science and engineering practitioners from academia, national laboratories and industry. With a commitment to helping K-12, undergraduate, female, and Underrepresented Minority (URM) populations in the Science, Technology, Engineering, and Mathematics (STEM) field through the interesting investment and rewarding education plan, this project lays out a comprehensive road map to prepare the next-generation high-performance graph analytics professional workers and researchers. This project revamps and creates core courses in both graduate and undergraduate levels for the PI's home department. To benefit the society at large, this project disseminates the project data, software, and publications to the broader research community at http://personal.stevens.edu/~hliu77/gsrw.html.\n\nThe overarching goal of this research is to make graph sampling and random walk fast, scalable and user-friendly. Towards that end, this career proposal advocates algorithm and system co-designed researches. First, this research introduces novel update and construction designs for transition probability of various major Monte Carlo methods that are essential for fast sampling. Second, to fully unleash the potential of GPUs, this project formulates the key primitive into problems that can take advantage of general, and reserved tensor and ray tracing cores on GPUs. Third, based upon the asynchronous processing nature of graph sampling and random walk, this research exploits Remote Direct Memory Access (RDMA)-assisted task and partition adaptive scheduling mechanism to reduce the data transfers for scalable trillion-edge graph sampling and random walk. Last but not the least, this career research delivers a bias-centric framework, which offers end users expressiveness to program not only a variety of exiting GSRW algorithms but also future ones, and simplicity by hiding the aforementioned advanced optimization techniques.\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": "10134", "attributes": { "award_id": "2049645", "title": "REU Site: Physics and Astronomy at the University of Minnesota", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Integrative Activities in Phys" ], "program_reference_codes": [], "program_officials": [ { "id": 1997, "first_name": "Kathleen", "last_name": "McCloud", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-15", "end_date": "2024-02-29", "award_amount": 378239, "principal_investigator": { "id": 26037, "first_name": "Alex", "last_name": "Kamenev", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "This site is supported by the Department of Defense in partnership with the NSF REU program.\n\nThis is an award for the Research Experience for Undergraduates (REU) program at the University of Minnesota’s Twin Cities campus School of Physics and Astronomy. It will enable ten undergraduate physics and astronomy majors to take part in cutting edge research by participating in a ten-week summer program. Each student will work in a research group, supervised by a faculty member, making the transition from classroom instruction to state-of-the-art research, from mastering old knowledge to creating new experiences. The project enhances opportunities for development and education for the involved students, on the one hand, and advances the progress of science, on the other. An additional focus emphasizes diversity, the participation of students from underrepresented groups, and first-generation college students. The majority of participants will come from smaller colleges, where they do not have the opportunity to participate in front-line research during the regular academic year. By encouraging students to participate in scientific research, particularly students from non-Ph.D. granting institutions, the summer REU program addresses a critical national need for skilled employees in Science, Technology, Engineering, and Mathematics (STEM) fields.\n\nThe specific projects will be in astrophysics, high-energy physics, condensed matter, and biophysics. Most of the student projects are anticipated to be in the area of experiment and data analysis. Some projects may focus on theoretical aspects of the areas listed above. In case the in-person participation proves to be problematic due to COVID19, the project leaders have prepared a log of projects that may be executed remotely with on-line interactions between participants and advisors. Most of such projects are in the area of data analysis of astrophysical or LHC-generated data.\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": "10135", "attributes": { "award_id": "2037673", "title": "Porous silicon on paper-based optical biosensor for diagnostics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EPMD-ElectrnPhoton&MagnDevices" ], "program_reference_codes": [], "program_officials": [ { "id": 1160, "first_name": "Usha", "last_name": "Varshney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-01", "end_date": "2024-03-31", "award_amount": 375000, "principal_investigator": { "id": 26039, "first_name": "Sharon", "last_name": "Weiss", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26038, "first_name": "Paul E", "last_name": "Laibinis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "There is a critical need for the development of cost-effective, highly sensitive, widely deployable, rapid diagnostic testing systems that can be adapted for detecting a variety of pathogens. This type of testing system can give healthcare providers key information necessary to make educated treatment decisions, and also can facilitate epidemiological studies of disease distribution patterns. This project investigates whether incorporation of a high surface area porous nanomaterial on a paper substrate can enable a new, highly sensitive, quantitative, and reliable platform for rapid diagnostic testing. The porous nanomaterial serves as the active sensing region that produces a clear optical signal change when signature molecules are selectively captured inside the pores, while the paper substrate is a simple and cost-effective fluid delivery vehicle that enables active transport of a test solution to the porous nanomaterial. The proposed work will lead to advanced understanding of fluid flow dynamics and molecular attachment in porous nanomaterials integrated with paper as well as advanced knowledge related to interfacing nanomaterials with non-traditional substrate materials. Educationally, this program will expose students to interdisciplinary research at the intersections of optics, materials science, engineering, and chemistry. A hands-on optical biosensor demonstration kit will be developed and deployed to K-12 students through shareable videos, classroom visits, and on-campus outreach activities. \n\nThe goal of this project is to demonstrate a porous silicon-on-paper optical biosensor capable of rapid, accurate, quantitative, and high sensitivity detection of protein biomarkers that will significantly advance the capabilities of rapid diagnostic testing. A comprehensive understanding of the achievable performance metrics, tolerances, and potential limitations of the porous silicon-on-paper optical biosensor platform will be attained. To accomplish this goal, key advances to realize porous silicon in a lateral flow configuration on paper and understand the fluid flow dynamics and molecular binding kinetics in such a configuration will be achieved. Specifically, this project seeks to: (1) develop a robust approach for integrating nanoscale porous films with paper-based microfluidic substrates; (2) understand molecular transport and binding kinetics in a porous film-on-paper platform as a function of the chemical and physical characteristics of the porous film and species infiltrated in the film; (3) establish the viability of using porous silicon optical thin films in a lateral flow rapid test framework; and (4) validate sensor through detection of SARS-CoV IgM and IgG antibodies.\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": 1385, "pages": 1394, "count": 13934 } } }