Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1393&sort=-principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1394&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-principal_investigator" }, "data": [ { "type": "Grant", "id": "604", "attributes": { "award_id": "2021909", "title": "BII-Design: Exploring the ecology and evolution of the global virome with big data and machine learning", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1334, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2022-12-31", "award_amount": 166189, "principal_investigator": { "id": 1336, "first_name": "Colin J", "last_name": "Carlson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1335, "first_name": "Tad", "last_name": "Dallas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "This Design activity will result in a proposal to create a Biology Integration Institute that will synthesize recent advances in wildlife virology and pursue new insights about the ecology and evolution of the global virome. The pandemic emergence of SARS-CoV-2 is only the latest development in an accelerating trend of dangerous viruses emerging from wildlife. Global travel, urbanization, and increasing human-wildlife contact have all made it easier for these viruses to emerge. In the future, climate change and land use change will reassemble the global virome even further, forcing mammals to cross continents, meet in new ecosystems, and exchange viruses thousands of times more, potentially unleashing even more threats to global health. At least 10,000 of these mammal viruses might have the potential to infect humans, but most of the global virome is still undescribed: only about 1% of mammal viruses have been discovered, and a much smaller fraction in other vertebrates. With so little data, it is difficult to predict which viruses will pose a future threat, or where, when, and how they could emerge. Predicting the next pandemic threat will require new data spanning biological scales, from single genes up to deep evolutionary time, and new statistical methods from the cutting edge of computer science and mathematics. In addition, the project will host summer residencies for trainees and develope new coursework that combines biology with hands-on computer science labs.The project assembles a group of virologists, computer scientists, statisticians, and ecologists to explore cutting edge scientific questions about methodology, inference, and impact. The project has three aims: (1) synthesizing existing data about host-virus associations for all vertebrate clades; (2) developing novel approaches to predict host-virus interaction networks, using novel data streams like viral strain diversity characterized from genomes, or receptor data from immunological studies; and (3) developing frameworks for actionable science that will put viral ecology to use for global health science and security. These aims will be accomplished through collaborative workshops. In doing so it will establish a foundation for a full Implementation proposal to develop an Emerging Virus Institute.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": "603", "attributes": { "award_id": "2024226", "title": "I-Corps: New image processing programs and data modeling algorithms for education environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1332, "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": "2020-08-01", "end_date": "2021-12-31", "award_amount": 50000, "principal_investigator": { "id": 1333, "first_name": "Amir K", "last_name": "Miri", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 321, "ror": "https://ror.org/049v69k10", "name": "Rowan University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 321, "ror": "https://ror.org/049v69k10", "name": "Rowan University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of an AI (artificial intelligence) solution that is aimed at enhancing student active learning. This technology platform is aimed at providing dynamic assessments of student performance throughout the academic year. The AI technology triggers timely interventions by providing early detection of struggling students as well as students with special talents. Unlike traditional platforms, this solution uses a combination of factors such as students’ behavior in the classroom, homework grades ,and regular test scores to evaluate risk levels and recommends generalized and personalized feedback plus identified routines for improving student learning performance. The platform also will communicate students’ progress to students/parents/teachers regularly. The proposed technology may enhance the learning experience by taking an approach that excludes the flaws of current system surfaced by the COVID-19 pandemic.This I-Corps project is based on the development of an AI (artificial intelligence) solution that uses advanced analytics to enhance students' learning performance. The proposed technology uses a a combination of AI tools such as computer vision, deep learning, machine learning, and natural language processing to thoroughly analyze students’ behavior inside and outside the classroom. It provides important prescriptive analytics and uses recommendation systems and collaborative filtering to provide dynamic feedback and identifies successful routines for improving the student learning performance. This project is based on several behavior data science studies and the power of advanced analytics for generating data-driven insights in education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "602", "attributes": { "award_id": "2033521", "title": "A1: KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1326, "first_name": "Lara", "last_name": "Campbell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 4998900, "principal_investigator": { "id": 1331, "first_name": "Krzysztof W", "last_name": "Janowicz", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1327, "first_name": "Dean A", "last_name": "Rehberger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1328, "first_name": "Pascal", "last_name": "Hitzler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1329, "first_name": "Wenwen", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1330, "first_name": "Mark P", "last_name": "Schildhauer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The goal of this project is to improve data-driven decision making and data analytics, specifically data analytics that involve geographic data. This project will create the “KnowWhereGraph” – a knowledge graph tool that specifically enables other data-analysis knowledge tools that have a geospatial component. GeoEnrichment describes the process by which data becomes augmented with a wide range of auxiliary information tailored to a geospatial study area (such as demographic data). GeoEnrichment tools significantly reduce the costs involved in acquiring, entering, and cleaning geo-data. Unfortunately, currently available geoenrichment services provide access to only pre-defined categories of information, do not effectively handle interconnected data, offer limited support for data integration, and are generally expensive. This project plans to make data-driven decision making and data analytics substantially more effective, accessible, and affordable. The project will merge novel Artificial Intelligence-based geoenrichment technologies with a knowledge graph that brings together open, cross-domain, densely integrated data spanning the human-environment interface. This project’s work is enabled by an open, freely usable knowledge graph. These graphs are a combination of scalable, Web-standard technologies, specifications, and data cultures for representing densely interconnected statements derived from structured or unstructured data across domains, in both human and machine-readable ways. The technology tools are designed to be useful to and useable by researchers, analysts, decision-makers, and the interested public in any domain or cross-domain activity requiring geospatial intelligence. This project includes strong partnerships with non-academic and academic stakeholders including 4 for-profit organizations, 2 government agencies, and one non-profit, as well as five academic partnerships: ESRI (Geographic Information Systems); Oliver Wyman, (commodity markets and supply chains), Princeton Climate Analytics (weather and climate information services), In10T (digital agriculture, farm partnerships); US Geological Survey (USGS), Natural Resources Conservation Service within the U.S. Department of Agriculture (USDA): and DirectRelief (humanitarian aid); as well as University of California Santa Barbara(UCSB), Kansas State University (K-State), Michigan State University (MSU), Arizona State University (ASU), and University of Southern California(USC). Additional partnerships are expected to develop during this Phase II effort. The “KnowWhereGraph” will be a valuable element of the Convergence Accelerator Phase II cohort, providing geospatial tools to the other projects within the cohort. In addition the project plans to focus on several strategic application areas that are likely to benefit US society, including: COVID-19 related supply chain disruptions and the US food, agriculture, and energy sectors, and their attendant supply chains generally; environmental policy issues relative to interactions among agricultural sustainability, soil conservation practice, and farm labor; and delivery of emergency humanitarian aid, within the US and internationally. Anytime knowing “where” is key, this project’s tools may be helpful. Formally, a knowledge graph consists of a massive set of statements, constructed from inter-connected node- and edge-labeled resources, allowing multiple, heterogeneous edges for the same nodes. A collection of definitional statements specifying the meaning of the knowledge graph's vocabulary is called its (KG) schema or ontology. The ontology is critical for rigorous logical interpretation and machine-actionability. Several innovations in knowledge graph technology will drive the project: (I) creating an open, web-accessible knowledge graph, with attendant methods and tools, to enable contributions to the graph from a range of sources; (II) developing strategies for semantically lifting imagery data, such as remotely sensed imagery and drone imagery, into this graph, thereby integrating vast amounts of data; (III) developing novel spatially-explicit AI-based methods, models, and services to enable geoenrichment on top of this graph; and (IV) developing both programmatic (application program interface, API) and human-accessible interfaces for the KnowWhereGraph. By merging the flexibility, expressive power, and community-driven features of open graph technologies with multi-format geospatial data and advanced geospatial intelligence, the KnowWhereGraph is designed to become a rich, integrative information resource that can transform and converge discovery, analysis, and synthesis within and across a multitude of fields and sectors.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": "14121", "attributes": { "award_id": "2120943", "title": "MCA: Career Advancement in Polar Cyberinfrastructure: Permafrost Feature Mapping and Change Detection using Geospatial Artificial Intelligence and Remote Sensing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Polar Cyberinfrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 30667, "first_name": "Marc", "last_name": "Stieglitz", "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": 359841, "principal_investigator": { "id": 1329, "first_name": "Wenwen", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "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/>Polar regions play a vital role in Earth’s climate, ecosystems, and economy. Unfortunately, climate change is driving dramatic changes in the Arctic ecosystem, endangering its natural environment, infrastructure, and lives. Arctic permafrost, ground that remains below 0°C for at least two consecutive summers, is at the center of this change. Covering nearly 1/4 of the land in the northern hemisphere, thawing permafrost is causing a significant local and regional impact in the Arctic. Severe impacts include land subsidence resulting in costly damage to the built environment and increased release of greenhouse gases which further exaggerates the greenhouse effect and global warming. To improve our understanding of permafrost dynamics and its linkages to other Arctic ecosystem components in the midst of rapid Arctic change, it is critically important to have geospatial data readily available that provide high-resolution mapping of permafrost features, their geographical extent, distribution, and change. Although a coarse classification of pan-Arctic permafrost has been developed, fine granularity, local to regional-scale mapping of major permafrost features, is largely unavailable. This data gap inevitably constrains us from gaining a holistic view of the space-time dynamics of permafrost degradation across the Arctic. The goal of this project is to bridge this existing data gap by developing new analytical solutions to support intelligent and automated delineation of permafrost features at scale.<br/><br/>Through a partnership with colleagues at Woodwell Climate Research Center, this project will explore novel ways to deepen the integration of cutting-edge AI, geospatial analysis, and cyberinfrastructure into Arctic permafrost research. Specifically, novel GeoAI (Geospatial Artificial Intelligence) solutions will be developed to empower the ongoing efforts of AI-based, high-resolution mapping of pan-Arctic permafrost thaw from Big Imagery. By enabling location-aware and multi-source deep learning and the integration of key spatial principles (i.e., spatial dependency and spatial autocorrelation), the proposed GeoAI model will create polar data products with high veracity and automation, thereby accelerating the scientific navigation of the New Arctic. A joint initiative, “Women in Polar Cyberinfrastructure,” will broaden the participation of women and underrepresented minorities in Arctic AI research. It will also serve as an important avenue for openly sharing knowledge and resources and provide mentorship to early-career scholars in Arctic science, GeoAI, and cyberinfrastructure. All datasets and tools produced in this project will be open-sourced and made available in the NSF Permafrost Discovery Gateway to increase their reuse and inspire further innovation.<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": "601", "attributes": { "award_id": "2024805", "title": "Student Travel Support for the 2020 IEEE SGComm Conference. To be Held November, 11-13, 2020 at Arizona State University.", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1323, "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": "2020-08-15", "end_date": "2022-07-31", "award_amount": 8750, "principal_investigator": { "id": 1325, "first_name": "Lalitha", "last_name": "Sankar", "orcid": "https://orcid.org/0000-0001-8122-5444", "emails": "[email protected]", "private_emails": "", "keywords": "['Statistical Learning Theory with focus on Loss Functions for learning']", "approved": true, "websites": "['https://sankar.engineering.asu.edu/', 'https://sankar.engineering.asu.edu/fact-federated-analytics-based-contact-traci…']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1324, "first_name": "Oliver", "last_name": "Kosut", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "The proposal requests support for students to participate in the 2020 IEEE SmartGridCommunications Conference (SGComm 2020), which was originally to take place in Tempe, AZ, October 6--9, 2020, but because of the COVID-19 pandemic will now be a virtual conference taking place from Nov. 11-13, 2020. The SGComm 2020, in its eleventh year, is a well established annual conference of the IEEE Communications Society focused on emerging data and information processing challenges in the electric power grid and energy systems. The conference offers opportunities to its participants for sharing of scientific discoveries related to various computing, communications, sensing, and information processing aspects of energy systems.SGComm 2020, a key conference of the IEEE Communications Society, has a theme this year entitled “Trustworthy Sensing, Communications, Processing and Analytics for a Sustainable Grid”. The conference will feature a comprehensive high-quality technical program including 4 symposia on Communications and Networking,Cyber Security and Privacy, Control and Operations, Grid Analytics and Computation as well as tutorials and workshops focused on emerging challenges and topics. The event will gather researchers and practitioners studying various aspects of the electric power grid including sensing, data processing, computing, communications, data analytics, and information sciences that are key today to ensure reliable and resilient grid operations. Finally, the conference also includes two keynote talks by leading researchers and a panel discussion, involving industry and academic leaders, on the ways to leverage technology and data science to address the key challenges for the new decade: grid resilience and sustainable energy use in the face of climate change.This proposal requests participation of about 25 students enrolled in US institutions (as well as US citizens attending international institutions) by providing registration fees to the conference. Such participation will have a positive impact on both grid-centric research in US institutions and workforce development. It will contribute to the preparation of the next cadre of engineering professionals who can advance and ensure integration of computing, data, security, and analytics technology in the electric grid while keeping the focus on sustainability, reliability, and resilience.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": "600", "attributes": { "award_id": "2033433", "title": "MIMO Radar With Sparse Linear Arrays - Theory, Implementation and Applications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1319, "first_name": "Zhengda", "last_name": "Zhengdao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 450000, "principal_investigator": { "id": 1322, "first_name": "Athina P", "last_name": "Petropulu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1320, "first_name": "Yingying", "last_name": "Chen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1321, "first_name": "Chung-Tse M", "last_name": "Wu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Multiple-input multiple-output (MIMO) radars have several advantages as compared to traditional phased arrays. They can achieve higher resolution with the same number of antennas. They can also achieve wide field of view, illuminating multiple targets at the same time, which translates to faster detection time. Reduction of the number of active antennas without hurting the radar performance would reduce the cost of the radar, while a low-cost, high resolution radar would advance the state-of-art of autonomous driving, smart environment, smart home, and IoT sensing, and would enable applications such as smart patient care, elderly monitoring, fitness assistant, etc., that rely on sensing. In an era where COVID-19 forced home isolation with limited supervision of vulnerable segments of the population, a radar device could provide information on vital signs, or detect falls without invading people's privacy in the way surveillance cameras would. MIMO radar using specially designed Sparse Linear Arrays (SLAs) can enjoy reduced hardware cost without losing the MIMO radar advantages. An SLA can be thought of as a uniform linear array with only a small number of active antennas. By careful selection of the active antennas and optimal design of transmit waveforms, one can maintain a radar performance close to that of the fully populated array. However, finding an optimal sparse array geometry in terms of the fewest antennas is a difficult combinatorial problem. The proposed project will advance the state-of-art of SLA based MIMO radar as a cost-effective imaging radar by (i) providing a novel framework for antenna selection, (ii) developing an SLA MIMO radar prototype based on frequency-scanning metamaterial (MTM) antennas, and (iii) developing real-time activity monitoring and user identification schemes that leverage the high resolution and wide field of view of MIMO SLA radar.There are several novel aspects in the proposed work. (i) A novel machine learning approach for antenna selection is proposed, which offers a unifying framework for dealing with any performance metric. The novelty of the proposed approach lies in its ability to get multiple softmax models to work together. (ii) The use of MTM antennas brings in the added advantage of allowing for easy change of the beam elevation by varying the antenna frequency. That advantages will be exploited to look for targets in the 3-D space while still using a linear array. By varying the frequency of the MTM antennas, one can select the elevation direction of the transmit beam, and by applying the proposed SLA design method, one can design the beam pattern in the 2-D space corresponding to the selected elevation direction. The frequency scanning capability resulting from the dispersive nature of MTM allows a real time and low complexity beam scanning mechanism, whereas the SLA MIMO radar with proper waveform engineering will generate a large scale virtual array with enhanced angular resolution. As such, the combination of SLA MIMO radar with MTM antennas will enable an unprecedented radar architecture with larger field of view, finer resolution, and small number of antenna RF fronts. (iii) Low-latency signal processing algorithms will be developed for leveraging the large field of view and high angle resolution, that will have the capability to construct 3D user models and identify multiple targets simultaneously. Innovative neural network structures will be devised to enable device-free user activity monitoring. It is expected that the multi-user identification mechanisms will reveal unique user-specific activity characteristics embedded in the movements of high-resolution point clouds, facilitating a broad range of emerging mobile applications.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": "599", "attributes": { "award_id": "2021546", "title": "Building the Foundation for Institutionalizing Course-Based Undergraduate Research Experiences in Community Colleges to Improve STEM Equity", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 1316, "first_name": "Michael", "last_name": "Davis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2022-09-30", "award_amount": 150000, "principal_investigator": { "id": 1318, "first_name": "Katrien", "last_name": "Kraft", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 319, "ror": "https://ror.org/0420yf673", "name": "Whatcom Community College", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1317, "first_name": "Jennifer M", "last_name": "Zovar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 319, "ror": "https://ror.org/0420yf673", "name": "Whatcom Community College", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by laying the foundation for increased access to research experiences at community colleges. Course-based Undergraduate Research Experiences (CUREs) enable students to do original research as part of their regular coursework. CUREs have the potential to advance equity goals by providing research experiences for all students, not just a select few. In addition, because two-year colleges have high enrollments of students from underrepresented groups, making CURES widely available has the potential to increase diversity in the STEM workforce. A group of faculty members at Whatcom Community College have been implementing CUREs in different STEM disciplines, including biology, geology, and engineering. An increasing number of other faculty are interested in implementing CUREs in their classes. The proposed project would leverage this interest to examine how the institution can widen the use of CUREs and, thus, generate an institutional transformation in STEM instructional practices. The two-year project will also contribute to national interests by completing a study on institutional change at a two-year college, which can inform the efforts of other institutions to improve STEM teaching and learning.This project is designed to: (1) identify Strengths, Weaknesses, Opportunities, and Threats (SWOT) for institutional capacity for CURE expansion and institutionalization; (2) support current CURE adopters through community building and feedback; (3) revisit the SWOT analysis to determine the institutional capacity to reform curricular efforts; and (4) disseminate key findings. The SWOT analysis will be part of a multi-day strategic planning retreat with CURE-implementing faculty and administrators. Participating CURE faculty members will develop a learning community to guide and support the process of developing CUREs. Considering the COVID-19 Pandemic, the project will have a focus on designing CURE experiences for online learning. The project will include a series of faculty interviews to provide rich descriptions of the mechanisms and reasons for implementing CUREs. Using these data, along with collaboration with an expert on institutional change, this project has the potential to produce a model of institutional improvement that is grounded in the experiences of faculty members at two-year colleges for which there is limited research. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.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": "598", "attributes": { "award_id": "2026128", "title": "SBIR Phase II: Nanostructured 3D Catalytic Coatings for High-Efficiency Pollution Control and Air Purification", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1314, "first_name": "Benaiah", "last_name": "Schrag", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2022-07-31", "award_amount": 1198140, "principal_investigator": { "id": 1315, "first_name": "Elijah", "last_name": "Shirman", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 318, "ror": "", "name": "Metalmark Innovations, Inc.", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 318, "ror": "", "name": "Metalmark Innovations, Inc.", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is addressing the growing societal need for indoor air purification, as indoor air quality (IAQ) directly affects human health, productivity, cognitive function, and quality-of-life. Awareness of the long-term consequences of poor IAQ has recently witnessed an increase due to research results, improvements in monitoring and sensing technology, public awareness campaigns by organizations such as the American Lung Association, World Health Organization, and Environmental Protection Agency, and, most recently, the COVID-19 pandemic. Finding sustainable, economical, and effective solutions to the problem of poor IAQ will greatly benefit public health and wellbeing and will help curtail the spread of pathogens, minimizing the need for social distancing. This project will develop a new system air purification, addressing viruses, harmful chemicals, odors, and ultrafine particulates.This Small Business Innovation Research (SBIR) Phase II project aims to scale up the production methodology and coating process of novel catalytic materials. The materials are 3D nanostructured porous powders that are designed at multiple length scales to achieve enhanced catalytic activity, stability, and longevity, while reducing costs and utilizing raw materials in an environmentally responsible manner. The system uses a synthetic approach based on self-organization of nanoscale building blocks and wet chemistry tools in order to assemble finely structured coatings for integration in air-purification units. This project focuses on expanding it to production scale, wherein achieving control over the composition, structure, porosity, and placement of nanoparticles on a production scale is challenging. The materials platform development will include adaptation to high-throughput instrumentation and scale-up of the material production and coating process to pilot production. In the process, this project will develop tools and guidelines for manufacturing hierarchically-structured functional materials more generally.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": "597", "attributes": { "award_id": "2026537", "title": "FW-HTF-P: Building a Skilled Technological Workforce in the Hospitality Service Industry of the Future", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1308, "first_name": "Alexandra", "last_name": "Medina-Borja", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2022-07-31", "award_amount": 146000, "principal_investigator": { "id": 1313, "first_name": "Jodi", "last_name": "Forlizzi", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1309, "first_name": "Howard M", "last_name": "Choset", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1310, "first_name": "George", "last_name": "Kantor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1311, "first_name": "Chinmay", "last_name": "Kulkarni", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1312, "first_name": "Sarah E", "last_name": "Fox", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This Future of Work at the Human-Technology frontier planning grant focuses on creating new ways for hospitality workers to shape how automation will affect their industry and their jobs. It has been widely predicted that the hospitality industry will experience significant job shifts in the coming decades. Automation plays a key role in causing these shifts, with examples including self-service kiosks, various applications of robotics, customer-facing AI, automated operations, and food preparation technologies. Such automation is already displacing many jobs in the hospitality industry. Although new jobs will be developed in the future, many others will be eliminated, and the need for new skill sets is rising. In the hospitality and service industries, automation has already affected hotel room check-in, cashiering, cleaning processes, bartending, and food service. This situation is changing even more rapidly as the work shifts in response to new standards around COVID-19 safety. Nearly 90% of hospitality workers lost work at least temporarily during the pandemic and they lack ways to envision and reap the positive impacts of technology innovation. The research team of experts in robotics, policy, economics, human-computer interaction, and organizational dynamics seeks to mitigate this growing problem by allowing hospitality workers to partner with and benefit from the future of automation and to maximize opportunities and positive outcomes for all of its stakeholders. The project will create generalizable knowledge through a series of workshops that bring together researchers, policy experts, union leaders and members, and stakeholders from leading hospitality schools. Specifically, the research team will assess the current state of hospitality services through a broad literature review, conduct workshops with key stakeholders, collectively explore technology innovations, and develop a strategic roadmap for future collaboration around automation technologies in the hospitality industry. This project has been funded by the NSF Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human-technology partnership in work contexts by advancing design of intelligent work technologies that operate in harmony with human workers.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": "596", "attributes": { "award_id": "2031942", "title": "Collaborative Research: Equity of Access to Computer Science: Factors Impacting the Characteristics and Success of Undergraduate CS Majors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 1306, "first_name": "Alexandra", "last_name": "Medina-Borja", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-15", "end_date": "2023-11-30", "award_amount": 498086, "principal_investigator": { "id": 1307, "first_name": "Cassandra", "last_name": "Guarino", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by improving undergraduate computer science education. It will do so by completing a research study that can reveal potential systemic limitations in access to computer science education by all students. This research study will examine ten-years of undergraduate student application, admissions, and retention data from four institutions. Analysis of these data will describe how students of varying demographics and pre-college preparation are present throughout the computer science talent pipeline. This study will fill an important research gap about factors that affect the flow of students into and through the computer science major. It is well documented that the demographic characteristics of computer science students are highly skewed toward males versus females and have skewed racial/ethnic distributions. What is not yet understood is at what point in the talent pipeline these imbalances are greatest and the degree to which they change as students progress through computer science undergraduate programs. In addition, the current educational disruption caused by COVID-19 provides the important and unique opportunity to determine what effect, if any, the resulting educational changes have had on participation of different groups of students in computer science. Students from underrepresented groups appear to have encountered greater difficulty accessing distance learning and being connected to the full range of educational opportunities presented by these unique circumstances, which are very strongly related to technological know-how. There is legitimate cause for concern that the pandemic will further divide the advantaged from the disadvantaged, further marginalizing the underrepresented groups that the project is studying from opportunities to advance into computer science majors and progress successfully through them. Computer science is an area of critical strategic importance for the nation, and a field in which cultivating domestic talent can have enormous impact. Thus, examining pre- and post- pandemic patterns of participation in computer science have the potential to help the nation meet its growing needs for talent in computer science and related fields, such as cybersecurity and artificial intelligence. This study will use a large, rich data set compiled from ten years of undergraduate application, admissions, and course-level data from four institutions: Loyola Marymount University, Cal State University Long Beach, the University of California Riverside, and the University of California San Diego. Analysis of these longitudinal data will improve understanding of who has access, who applies, who is admitted, and who succeeds in computer science. Using classical statistical approaches and modern machine learning based approaches to analysis of large data sets, the study seeks to understand how to improve the inclusion of all students in computer science. It will supplement this large-scale quantitative analysis with qualitative analysis of results from targeted focus-groups and interviews. The qualitative analysis, coupled with the quantitative analysis of longitudinal data from four institutions with different student demographics and other characteristics, will provide a deeper analysis of access to and success in computer science than any previous study. The resulting extension of knowledge has the potential to lay the foundation for achieving equitable access to computer science education for all students. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.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": 1393, "pages": 1424, "count": 14236 } } }