Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1386&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1387&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10447", "attributes": { "award_id": "2133205", "title": "Collaborative Research: Optimized Testing Strategies for Fighting Pandemics: Fundamental Limits and Efficient Algorithms", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 26403, "first_name": "Huaiyu", "last_name": "Dai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2025-08-31", "award_amount": 225000, "principal_investigator": { "id": 4638, "first_name": "Weiyu", "last_name": "Xu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 220, "ror": "https://ror.org/036jqmy94", "name": "University of Iowa", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 220, "ror": "https://ror.org/036jqmy94", "name": "University of Iowa", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true }, "abstract": "Large-scale high-throughput prevalence and diagnostic testing is essential for the containment and mitigation of pandemics. The testing bottleneck in the COVID-19 pandemic has led to a resurgence of interest in group testing, where several people's biological samples are mixed together and examined in a single test. When the rate of infection in the population is low, this method can significantly reduce the total number of tests per subject and increase the throughput of the existing testing infrastructure. However, traditional group testing has the following limitations: First, efficient group testing based methods for the estimation of prevalence have been largely overlooked in the literature. Second, traditional group testing usually assumes that the testing results are qualitative (positive versus negative), not quantitative (providing viral load information). Third, the theoretical study of group testing rarely takes practical constraints, such as the sensitivity of the pooled tests and the dilution effect, into consideration, which hinders the applicability of the testing schemes in practice. The goal of this project is to overcome these limitations of traditional group testing and design advanced pooled testing strategies for efficient prevalence tracking and accurate infection diagnosis. It will develop optimized pooled testing strategies with strong theoretical performance guarantees yet feasible and cost-effective in practice.\n\nThe proposed research is organized in three research thrusts as follows. Thrust 1 aims to design effective sampling and testing algorithms to estimate the prevalence in communities and track its evolution, under scarce testing resource constraints. Thrust 2 focuses on the design of optimized pooling and decoding algorithms for compressed sensing based (COVID-19) virus diagnostic testing. Thrust 3 validates the accuracy and efficiency of the proposed pooled testing through experiments on anonymized COVID-19 samples. This project bridges group testing and online learning, the two largely disconnected areas, with the objective to effectively allocate limited testing resources for efficient prevalence tracking. Such integration leads to novel sampling strategies, broadens the paradigm of group testing, and advances the state of the art of online learning. Moreover, the proposed compressed sensing based diagnostic testing leverages quantitative measurements provided by advanced testing technologies, which can significantly increase test throughput, reduce the number of needed tests, decrease the consumption of scarce reagents, and provide results robust against observation noises and outliers. The rich compressed sensing theory provides possible approaches to the rigorous mathematical certification of the correctness of the decoded results. Besides, the clinical constraints on pooled testing also lead to novel problem formulation and theoretical characterization, enriching the study of compressed sensing.\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": "10448", "attributes": { "award_id": "2136674", "title": "STTR Phase I: Low Cost, Large Scale Production of Biocidal Micropowder by a Reversed Arc, Plasma-Fluidized Bed Reactor", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "STTR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 670, "first_name": "Anna", "last_name": "Brady", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2023-05-31", "award_amount": 255966, "principal_investigator": { "id": 26405, "first_name": "Vladimir", "last_name": "Gorokhovsky", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 10282, "first_name": "Tobin L", "last_name": "Munsat", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1928, "ror": "", "name": "NANO-PRODUCT ENGINEERING, LLC", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is based on the improvement of respiratory equipment and personal protective equipment (PPE). Commercially available face masks, gowns, filter media and other PPE are contaminated through environmental exposure, posing a threat to healthcare personnel. The proposed solution is a high-surface-area antiviral/antimicrobial micropowder which can be applied to PPE surfaces. Improved protection from infections and reduced cross-contamination may result in fewer deaths, decreased healthcare costs, and increased economic productivity. The addressable global PPE market was $23 billion in 2020 and is expected to grow through 2028, while the global disposable face mask market was valued at $792 million in 2019, and the inclusion of other types of PPE increases this estimate further. The COVID-19 pandemic is driving the demand for these products, though the applicability of the proposed technology is not limited to COVID-19. The proposed product will be material that can modify the surfaces of fabrics with active antiviral properties, leading to improvements in health and safety. The customer base is expected to be healthcare personnel, pharmaceutical and food manufacturing facilities, the public, and manufacturers of PPE, medical devices, textiles, and filters.\n\nThis STTR Phase I project proposes to improve the antiviral/antimicrobial properties of respiratory equipment and PPE, which is typically only passively protective, is difficult to decontaminate and re-use, and can lead to worker exposure during changing and handling. The proposed solution is a high-surface-area micropowder, coated with an antiviral/antimicrobial coating, which can be applied to PPE surfaces. Unlike current technologies, the particle size, morphology, surface area, and topography can all be tailored for specific biocidal activity. Additionally, powder shape and surface quality can be customized to enhance adhesion. The proposed project is to develop a prototype powder product formulation that allows easy application of antiviral (copper alloy) coated particles to fabrics and other materials. This technology is based on a fluidized-bed, plasma-enhanced deposition process for synthesizing unique core-shell micropowders of metal, ceramic, or highly-shaped nanoforms of carbon, including proven biocidal materials. The synthesized microparticles will have high surface-to-weight ratios making them better suited for capturing micro-organisms while also having improved bonding to surfaces of filter media and PPE materials.\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": "10449", "attributes": { "award_id": "2139716", "title": "U.S.-Ireland R&D Partnership: Wearable Dynamic Microsystem Sampler for Collecting Microbial Volatiles (SenSorp)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 763, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2025-08-31", "award_amount": 390000, "principal_investigator": { "id": 26443, "first_name": "Masoud", "last_name": "Agah", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The characterization and monitoring of volatile organic compounds (VOCs) emitted from different sources is immensely important in different disciplines. One of these applications is the analysis of VOCs emitted from human skin for biomarker discovery and disease diagnosis. Skin performs critical functions including protection of internal living tissue and other organs, establishes contact to the external environment and has high metabolic activity. Systemic as well as localized skin diseases are known to modify the molecular and microbial composition of human skin. Consequently, the skin is becoming recognized as a wealthy source of diagnostic information regarding physiological status. There is evidence of canines having a sophisticated olfactory ability to detect the presence of COVID-19 disease from the volatile emission of body and the role of skin volatiles in canine sensing of epilepsy attacks is thought to be significant. It is important to develop new robust and smart analytical workflows to study skin VOCs with the goal of new volatile biomarker identification for wearable bio-diagnostics. \n\nThe proposed research provides the first-of-its-kind approach for standardizing skin scent collection and develops a readily deployable system as small as an apple watch for point-of-care skin VOC collection. The proposed SenSorp is fabricated using microelectromechanical system (MEMS) and 3D printing technologies and includes a sampler to collect VOCs from skin and a sensor to track the amount of VOCs collected. SenSorp measures the collected VOCs in real time and notifies the user about the collected mass through its electronic circuity embedded within the SenSorp’s Smart Key. SenSorp is equipped with a novel 3D printed package that can prevent the adsorption of VOCs from the environment during sample collection from skin and during the storage via its embedded valving mechanism. The rotation of the Smart Key will open or close the VOC pathway from outside (skin/environment) to the adsorption materials in the SenSorp. The SenSorp Auto-Injector is the interface module to commercial gas chromatography instruments for identification of VOCs present in the skin. The validation of the system in controlled microbial environments with subsequent human subject studies is a key step toward the final objective of this collaborative effort, which is to make skin scent a robust medium for disease biomarker discovery and disease diagnosis. The outcome of this project will set an outstanding example of how microscale engineering and analytical chemistry can become highly complementary methodologies to develop low-cost, accessible platforms for biomarker discovery and disease diagnosis. This research will advance discovery while promoting teaching and learning at the undergraduate and graduate levels. The outcome of this research will be integrated with Virginia Tech’s outreach programs targeting mainly under-represented groups in STEM led by the Center for the Enhancement of Engineering Diversity (CEED). There will be wide dissemination of the research outcomes to the engineering and scientific communities in peer-reviewed journals, in presentation at multidisciplinary conferences, and in social media (Youtube, LinkedIn, and Clubhouse).\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": "10450", "attributes": { "award_id": "2213033", "title": "SBIR Phase I: Low cost, portable mass spectrometers based on a chip-scale ion trap mass analyzer", "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": 1211, "first_name": "Peter", "last_name": "Atherton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2023-08-31", "award_amount": 256000, "principal_investigator": { "id": 26444, "first_name": "Wade", "last_name": "Rellergert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1934, "ror": "", "name": "IONICSCALE LLC", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to greatly expand the power of mass spectrometry for chemical detection and analysis to a far broader user base. The ultimate goal is to produce a chip-scale device sufficiently affordable that it can be a user replaceable component in an ultra-compact instrument. This is enabled by a microfabricatable ion trap geometry that circumvents key shortcomings of previous chip-scale mass analyzer efforts. There is potential to bring this technology to the consumer market where it can inform household residents of harmful trace or odorless chemicals present in their homes. In addition, with advances in data science, the proposed technology might also be able to inform household residents of volatile organic compound signatures that may be indicative of the early onset of disease in a manner similar to dogs which have a demonstrated ability to smell certain types of cancer, Parkinson’s disease, spiking or dropping blood sugar levels in diabetics, and, most topically, CoVID-19, among other conditions. Prior to entry into the consumer market, these compact instruments can be leveraged for important in-situ analytics in fields such as defense, energy production, pharmaceutical research, environmental monitoring, and space exploration. \n\nThis Small Business Innovation Research (SBIR) Phase I project will enable the assessment of a novel, patent-pending ion trap mass analyzer in a compact physics package for handheld mass spectrometers. Mass spectrometers are the gold standard for chemical analysis and have wide ranging applications, however, widespread utilization of these powerful instruments is hindered by their high cost, size, weight, and power. Current portable instruments are ~$100k USD, roughly the size of a small suitcase, and operate for only a few hours on a single battery charge. The novel ion trap mass analyzer geometry explored as part of this work can be microfabricated, potentially lowering the cost per unit to the point where it can be incorporated in an instrument physics package that is a replaceable cartridge, thus eliminating the need for expert maintenance. Coupled with modern computational methods, processing power, and cloud computing architectures, these mass spectrometers could be utilized for chemical analysis applications for which mass spectrometry is currently not a cost-effective solution. This dream of ubiquitous, high specificity chemical analysis technology could generate massive amounts of new raw data informing and creating future collective research and advanced applications/solutions.\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": "10451", "attributes": { "award_id": "2200161", "title": "PIPP Phase I: Computational Foundations for Bio-social Modeling of Unseen Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 1123, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2024-02-29", "award_amount": 897531, "principal_investigator": { "id": 26449, "first_name": "Pavan", "last_name": "Turaga", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26445, "first_name": "Giulia", "last_name": "Pedrielli", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26446, "first_name": "Gautam", "last_name": "Dasarathy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26447, "first_name": "Visar", "last_name": "Berisha", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26448, "first_name": "Patricia A", "last_name": "Solis", "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": "Pandemics unfold in a social, behavioral, and decision-making context that alters the geospatial patterns of spread, depending on an existing underlying landscape of risk and adaptive behavior. Layering socioeconomic factors into traditional predictive modeling frameworks is not sufficient to understand this complexity, nor does it account for the dynamics and vicissitudes of human behavior and free-will. Unexpected human behaviors play a major role, as well as broader factors such as vaccine availability, seasonal effects from human contact patterns, viral environmental persistence, and federal and state-level policy changes around masking and business closures. Enumerating a finite list of factors that should form the basis of a predictive model itself seems like a grand challenge. This project will advance modeling as a continuous, iterative, and dynamic component of pandemic response, where incremental predictions are far more robust, and approaches that innately allow for complexity, adaptation, and surprise can be expected to be operationally useful.\n\nPandemic prevention for unseen pandemics requires several interconnected efforts across immunology, mechanistic modeling, data-driven modeling, and understanding sociopolitical contexts of decision making. Technical aspects of the project include machine learning based tools for predicting immune response from pathogen mutations, switching dynamical systems based models of time-series for fast adaptation, adaptive population sampling techniques, and model predictive control methods for designing behavioral interventions. The project will develop integrative protocols and frameworks that a) leverage techniques for using binding patterns of pathogens for never-before-seen viruses and advances in wastewater-based epidemiology, b) understand the variation in performance of predictive models over geospatial scales using regularizing models, c) design effective interventions under resource constraints, and d) understand their impact on policy making. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG).\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": "10452", "attributes": { "award_id": "2218809", "title": "SCC-IRG Track 1: Preparing for Future Pandemics: Subway Crowd Management to Minimize Airborne Transmission of Respiratory Viruses (Way-CARE)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 975, "first_name": "Yueyue", "last_name": "Fan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-01-01", "end_date": "2026-12-31", "award_amount": 2500000, "principal_investigator": { "id": 26453, "first_name": "Xuan", "last_name": "Di", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4669, "first_name": "Jeffrey L", "last_name": "Shaman", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Emerging Infections']", "approved": true, "websites": "['https://blogs.cuit.columbia.edu/jls106/publications/covid-19-findings-simulatio…', 'https://github.com/shaman-lab/COVID-19Projection']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, { "id": 26450, "first_name": "Xiaofan", "last_name": "Jiang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26451, "first_name": "Kai", "last_name": "Ruggeri", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26452, "first_name": "Marco G", "last_name": "Giometto", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This Smart and Connected Communities (S&CC) project focuses on strengthening the preparedness and resilience of transit communities facing public health disasters through the development of a sociotechnical system for crowd management. Following the substantial drop in public transportation ridership across the globe during the pandemic, how can subway systems respond to and recover from a future pandemic? Mass transit, especially subways, are essential to the economic viability and environmental sustainability of cities. This research will elevate U.S. leadership and economic competitiveness in recovery from pandemics, and will improve the social, economic, and environmental well-being of those who live, work, and travel within cities. The goal of this study is to equip public transit communities (i.e., agencies, workers, and riders) with a sociotechnical system, “Way-CARE\" (Subway Crowd Management to Minimize Airborne Transmission of REspiratory Viruses) that: 1) enables transit riders to make informed decisions and adapt travel behavior accordingly; and 2) provides transit agencies engaged in planning and policymaking with recommendations for mitigating virus transmission risks to riders and workers. People in low-income communities are among the most impacted and are in a disadvantaged position due to reduced accessibility to perceived safer travel modes. As such, the broader impacts of this study include helping identify needs, target resources, and develop more effective approaches to better ensure health and wellness, accessibility and inclusivity, and economic vitality for residents of low-income communities. The accompanying educational plan aims to broaden participation in engineering of underrepresented groups via outreach programs, including programs for Harlem public school teachers and K-12 students, as well as annual student data science challenges.\n\nTrue health risks inside subway systems and future commuting patterns are unknown after the pandemic. The technological propeller of the project is the integration of sensing, crowd and airflow modeling, and public health knowledge on a microscale applied to subway crowd management. Coupled airborne dispersion and epidemiological models will be developed that account for microscale processes (transport of droplets and aerosols) affecting respiratory virus transmission opportunities. The social catalyst of the award is the integration of behavioral science evidence to inform travel choices and policy making. The Metropolitan Transportation Authority (MTA) and two local rider communities (Harlem and Columbia) will be engaged in the development and assessment of the sociotechnical dimensions of the project. To assure project success, a 2-phase evaluation plan is presented to pilot the system and the technologies. Transferability and scalability will be investigated with input from the engaged communities.\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": "10453", "attributes": { "award_id": "2222129", "title": "Collaborative Research: FW-HTF-R: Toward an Ecosystem of Artificial Intelligence-Powered Music Production (TEAMuP)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "FW-HTF Futr Wrk Hum-Tech Frntr" ], "program_reference_codes": [], "program_officials": [ { "id": 849, "first_name": "Dan", "last_name": "Cosley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2026-09-30", "award_amount": 1413858, "principal_investigator": { "id": 25876, "first_name": "Raffaella", "last_name": "Borasi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26454, "first_name": "Zhiyao", "last_name": "Duan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26455, "first_name": "Jonathan", "last_name": "Herington", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26456, "first_name": "Rachel", "last_name": "Roberts", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This project builds the foundations of a new ecosystem for music production to empower future musicians to better leverage Artificial Intelligence (AI) tools in the creation, performance, and dissemination of their music, while also accelerating audio AI research. This involves the creation of both an open-access software framework enabling musicians and researchers to collaborate in the development and use of ever-better AI-powered tools for music creation, and a set of initiatives to enable a critical mass of musicians to use these tools in transformative ways. Musicians are expected to use these tools to produce lower-cost, higher-quality music products, which meet growing demand for digital music content for videos, websites, advertising, audio recordings, and other new media. Enabling musicians to be more self-sufficient in their music creation has the potential to increase the number of musically talented individuals that will be able to make a living with their art, especially from currently under-represented populations. To enable growing musicians to make full use of AI tools, a set of innovative learning experiences to acquire the needed mindsets and skills will be developed and field tested in a 2-semester course for students with music interests and a “Summer Camp” for pre-college under-represented youth, along with the creation of online instructional materials to support specific learning experiences in a variety of settings.\n \nThe project team possesses complementary disciplinary expertise in music, audio-engineering, AI, learning sciences/ education, business/ entrepreneurship, ethics, and inclusion. These skills will be brought to bear on developing a framework for a commonly-used free and open-source digital audio platform that will allow: (a) audio AI researchers to easily deploy their new AI models into the platform; and, b) musicians who use these AI tools to share their music productions with AI researchers so they can refine their models. Interviews and surveys will also be conducted with diverse musicians to better understand key factors that may affect their adoption of AI music production tools and how those tools may transform their work, as well as the implications of the pandemic and other barriers that may be experienced by under-represented populations in music production. Together, the project will generate a better understanding of factors that may affect musicians’ adoption and transformative use of AI in their work, understanding which could be generalized to other occupations at the human-technology frontier. Finally, the team will develop pedagogical principles and practices that can inform the design of effective educational interventions to better prepare future musicians and other domain experts to leverage technology.\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": "10454", "attributes": { "award_id": "2222369", "title": "Collaborative Research: FW-HTF-R: Toward an Ecosystem of Artificial-intelligence-powered Music Production (TEAMuP)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "FW-HTF Futr Wrk Hum-Tech Frntr" ], "program_reference_codes": [], "program_officials": [ { "id": 849, "first_name": "Dan", "last_name": "Cosley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2026-09-30", "award_amount": 386139, "principal_investigator": { "id": 26457, "first_name": "Bryan", "last_name": "Pardo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 317, "ror": "https://ror.org/000e0be47", "name": "Northwestern University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This project builds the foundations of a new ecosystem for music production to empower future musicians to better leverage Artificial Intelligence (AI) tools in the creation, performance, and dissemination of their music, while also accelerating audio AI research. This involves the creation of both an open-access software framework enabling musicians and researchers to collaborate in the development and use of ever-better AI-powered tools for music creation, and a set of initiatives to enable a critical mass of musicians to use these tools in transformative ways. Musicians are expected to use these tools to produce lower-cost, higher-quality music products, which meet growing demand for digital music content for videos, websites, advertising, audio recordings, and other new media. Enabling musicians to be more self-sufficient in their music creation has the potential to increase the number of musically talented individuals that will be able to make a living with their art, especially from currently under-represented populations. To enable growing musicians to make full use of AI tools, a set of innovative learning experiences to acquire the needed mindsets and skills will be developed and field tested in a 2-semester course for students with music interests and a “Summer Camp” for pre-college under-represented youth, along with the creation of online instructional materials to support specific learning experiences in a variety of settings.\n \nThe project team possesses complementary disciplinary expertise in music, audio-engineering, AI, learning sciences/ education, business/ entrepreneurship, ethics, and inclusion. These skills will be brought to bear on developing a framework for a commonly-used free and open-source digital audio platform that will allow: (a) audio AI researchers to easily deploy their new AI models into the platform; and, b) musicians who use these AI tools to share their music productions with AI researchers so they can refine their models. Interviews and surveys will also be conducted with diverse musicians to better understand key factors that may affect their adoption of AI music production tools and how those tools may transform their work, as well as the implications of the pandemic and other barriers that may be experienced by under-represented populations in music production. Together, the project will generate a better understanding of factors that may affect musicians’ adoption and transformative use of AI in their work, understanding which could be generalized to other occupations at the human-technology frontier. Finally, the team will develop pedagogical principles and practices that can inform the design of effective educational interventions to better prepare future musicians and other domain experts to leverage technology.\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": "10455", "attributes": { "award_id": "2050542", "title": "Building Capacity to Increase the Pool of Highly Qualified STEM Teachers in High-Need Texas School Districts with Predominantly Hispanic Student Populations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Robert Noyce Scholarship Pgm" ], "program_reference_codes": [], "program_officials": [ { "id": 1859, "first_name": "Mike", "last_name": "Ferrara", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2022-05-31", "award_amount": 74970, "principal_investigator": { "id": 26459, "first_name": "David", "last_name": "Turner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26458, "first_name": "Angeli M", "last_name": "Willson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 404, "ror": "", "name": "St Mary's University San Antonio", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to address the need for science, technology, engineering, and mathematics (STEM) K-12 teachers at both the national and regional level. Toward this aim, St. Mary’s University, a four-year Hispanic Serving Institution in San Antonio, Texas, will work with local education agency partners to evaluate and build capacity within its STEM Teacher Certification programs. The project will focus broadly on improving the pipeline for STEM majors seeking teacher licensure. In addition, it will encourage STEM pre-service teachers to teach in high-need schools and develop strategies and resources to support STEM pre-service teachers through their undergraduate studies and early stages of their professional K-12 teaching careers. This effort will include a careful investigation of available resources that might influence the development of an effective teacher preparation program and partnerships with local school districts. The project will also work to understand factors that impact Hispanic and other students as they make educational and career choices. \n\nThis project at St. Mary’s University includes partnerships with the Northside Independent School District (Northside ISD) and the San Antonio ISD. Project goals include: 1) analyzing baseline data to explore the need for highly qualified STEM teachers in the partner local education agencies; 2) understanding the motivations and obstacles faced by Hispanic and other students as they make educational and career choices, particularly whether to major in STEM fields and to pursue a career as a K-12 STEM educator; 3) evaluating the existing university infrastructure for a robust program for student recruitment and STEM teacher preparation; and 4) developing a comprehensive plan to enhance the University’s ability to (a) successfully recruit, retain, and graduate talented students as STEM teachers; and (b) to support them during their first year as teachers in high-needs school districts. The project will generate new knowledge about effective recruiting and support mechanisms for diverse pre-service teachers, in partnership with school districts that have predominantly Hispanic student populations. Project outcomes are expected to contribute to ongoing efforts to broaden participation in K-12 STEM teaching. This Capacity Building project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the persistence, retention, and effectiveness of K-12 STEM teachers in high-need school districts.\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": "10456", "attributes": { "award_id": "2105958", "title": "I-Corps: Aquatic debris cleanup using multi-agent unmanned surface vehicles and hotspot-based path optimization", "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": "2021-03-15", "end_date": "2022-08-31", "award_amount": 50000, "principal_investigator": { "id": 26460, "first_name": "Jan", "last_name": "Kleissl", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is to benefit US shorelines. The World Economic Forum predicts that by 2050 there will be more plastic than fish in the world's oceans. Waterfront maintenance workers face the brunt of the marine trash pollution issue as 80% of marine trash is sourced near-shore. As a result, waterfront governments alone spend a total of $10.2 billion to clean these waterways. The proposed technology benefits ecosystems by removing trash pollution from our shores, thereby preventing ecosystem degradation and allowing people to both benefit from marine services now and in the future. \n\nThis I-Corps project introduces the use of Unmanned Surface Vehicles (USV) to maximize debris cleanup performance and provide affordable solutions to cleaning these waterways. Solutions are being developed to address the oceanic plastic pollution problem once the debris is already in the ocean; however, insufficient effort is directed towards improving shoreline pickup, which contains 80% of plastic debris. Human-operated methods to collect the debris is inefficient and cost ineffective. This opens the door to expand USV technology to waterway maintenance. Similarly, USV environmental monitoring capabilities may provide data to the waterways and researchers such as oxygen monitoring for algae blooms and affordable data capture. The team currently has a working prototype and partnerships at the Jacobs School of Engineering and Scripps Institution of Oceanography. The I-Corps customer discovery activities will reveal the exact pain points experienced in these waterways to inform the proposed technology.\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": 1386, "pages": 1419, "count": 14184 } } }