Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=4&sort=-program_reference_codes
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-program_reference_codes", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1397&sort=-program_reference_codes", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=5&sort=-program_reference_codes", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=-program_reference_codes" }, "data": [ { "type": "Grant", "id": "1710", "attributes": { "award_id": "2041096", "title": "EAGER: Collaborative Research: Understanding Human Behaviors and Mental Health using Federated Machine Learning on Smart Phones", "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": [ "7364", "7916" ], "program_officials": [ { "id": 4479, "first_name": "Sylvia", "last_name": "Spengler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-02-28", "award_amount": 75000, "principal_investigator": { "id": 4480, "first_name": "Hai", "last_name": "Phan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding human behaviors and mental health are becoming increasingly important for modern society. The ongoing outbreak of coronavirus (COVID-19) not only further highlights its importance but also calls for immediate action. This project will develop a federated machine-learning (FL) framework and application on mobile device for understanding human behaviors and mental health. The planned research will synergize interdisciplinary research and particularly push the envelopes of federated learning and public health. This project will not only provide an important and timely real-world application, health-behavior monitoring and prediction, for the federated learning community, but also will advance our understanding of physical and mental health through mobile devices, and the impacts of COVID-19 to human society in a unique and detailed angle. This project will integrate the interdisciplinary research results into courses, and train students from underrepresented groups. Technically, the project has two main components: 1) Data collection and statistical analysis, and 2) Building federated learning framework and application. In the first component, the project will collect smartphone-based sensor data from student sub-population in both urban and suburban areas along with other health related surveys and data. The project will specifically analyze and determine what data collected from the mobile phone can be the indictors and causal factors of behavior and mental health. In the second component, the project will develop deep learning models to predict human behaviors, physical and mental health conditions/trends over time, under rigorous privacy protection. Specifically, the prediction models will be developed in federated learning settings to train the model locally on the device with differential privacy guarantees, without collecting sensor data to the cloud. Finally, the project will develop a federated learning based behavior monitoring and prediction application on mobile phones and will evaluate the prototype system on the cohort of studies from first component.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": "1862", "attributes": { "award_id": "2041415", "title": "EAGER: NewsStand CoronaViz: A Map Query Interface for Tracking the Spread of COVID-19", "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": [ "7364", "7916" ], "program_officials": [ { "id": 4925, "first_name": "Sylvia", "last_name": "Spengler", "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": 4926, "first_name": "Hanan", "last_name": "Samet", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "With the rapid continuing spread of COVID-19, it is clearly important to be able to track its progress over time in order to be better prepared to anticipate its emergence and spread in new regions as well as declines in its presence in regions thereby leading to or justifying \"reopening\" decisions. There are many applications and web sites that monitor officially released numbers of cases which are likely to be the most accurate methods for tracking the progress of COVID-19; however, they do not necessarily paint a complete picture. To begin filling any gaps in official reports, the project will develop the NewsStand CoronaViz (abbreviated as CoronaViz) web application aimed at allowing users to explore the geographic spread in discussions about COVID-19 through analysis of keyword prevalence in geotagged news articles and tweets in relation to the real spread of COVID-19 as measured by the confirmed cases numbers reported by the authorities.CoronaViz users will have access to dynamic variants of the disease-related variables corresponding to the number of confirmed cases, active cases, deaths, and recoveries (where they are provided) via a map query interface. They will also have the ability to step forward and backward in time using both a variety of temporal window sizes (day, week, month, or combinations thereof) in addition to user-defined varying spatial window sizes specified by direct manipulation actions (e.g., pan, zoom, and hover) as well as textually (e.g., by the name of the containing continent, country, state or province, or county). The result is an animation and that also supports zooming which means that users zoom in on a map they get more data rather than magnified data as is the case in most existing systems. CoronaViz is not restricted to COVID-19 and can be used for other diseases such as Ebola. Having a system such as CoronaViz is useful should the COVID-19 pandemic return.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": "1669", "attributes": { "award_id": "2040459", "title": "EAGER: Spatiotemporal Big Data Analysis to Understand COVID-19 Effects", "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": [ "7364", "7916" ], "program_officials": [ { "id": 4373, "first_name": "Wei", "last_name": "Ding", "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": 99996, "principal_investigator": { "id": 4374, "first_name": "Shashi", "last_name": "Shekhar", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Data Mining']", "approved": true, "websites": "['https://www-users.cs.umn.edu/~shekhar/', 'http://www.spatial.cs.umn.edu/Project/covid_19.html']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has impacted public health with a large number of mortalities and ravaged the economy by increasing unemployment to a historically high level. The goal of this project is to investigate the potential for novel spatiotemporal big data to assist in identifying COVID-19 related geographic patterns, such as locations where groups of people visit for long, overlapping times, and travel to and from hotspots. Such patterns are of great interest to policy-makers and public health researchers, but are difficult to find in traditional mobility datasets such as infrequent travel surveys and urban highway traffic data. Example spatiotemporal big data include privacy-protected aggregated location traces of mobile devices that have recently been opened for COVID-19 research. If successful, the results will inform disease spread models and policy-interventions to save lives and reopen the economy safely.This project is expected to result in multiple data science and computer science innovations. First, it will define and quantify hangout-venues, a novel spatiotemporal pattern family modeling the places with many overlapping long visits. Examples include full-service dine-in restaurants which have many long visits, but not limited-service restaurants which mostly have short visits. Second, it will probe new interest measures to not only distinguish between patterns (e.g., full-service restaurants) and non-patterns (e.g., limited-service restaurants) but also support the design of computationally efficient algorithms based on properties such as anti-monotone. Third, it will design novel and scalable algorithms for analyzing the large (tens of terabytes) dataset for hangout-venues. Fourth, it will investigate the impact of selection bias and noise from differential privacy schemes. The results have the potential to transform data science knowledge with novel pattern families (e.g., hangout-venue) and improve the understanding of the impact of selection bias and noise added by differential privacy schemes on pattern mining methods and their results. Furthermore, the project will co-produce knowledge in close collaboration with public health researchers and policymakers. The results have the potential to transform the understanding of the public mobility for modeling disease transmission dynamics by leveraging the emerging spatiotemporal big data.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": "1665", "attributes": { "award_id": "2040144", "title": "EAGER: An AI-driven Paradigm for Collective and Collaborative Community Resilience in the COVID-19 Era and Beyond", "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": [ "7364", "7916" ], "program_officials": [ { "id": 4364, "first_name": "Wei", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2022-01-31", "award_amount": 16562, "principal_investigator": { "id": 4366, "first_name": "Yanfang", "last_name": "Ye", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4365, "first_name": "Kenneth", "last_name": "Loparo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 497, "ror": "https://ror.org/051fd9666", "name": "Case Western Reserve University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "The coronavirus disease (COVID-19) pandemic has exposed a critical set of vulnerabilities that have impacted community resilience in responding to escalating societal, economic, and behavioral issues. Unfortunately, there are no established solutions or proven models for us to depend on to tackle the complex challenges with significant uncertainties and unknowns. This project engages novel disciplinary perspectives to help address the devastating effects caused by COVID-19, i.e., leveraging the extracted information of experiences, ideas and support from positive-energy communities who are successfully navigating threats that can be transformed and transferred into actionable information to assist vulnerable communities to cope, progress and move forward. More specifically, by advancing artificial intelligence (AI) innovations, the goal of this project is to design and develop an AI-driven paradigm for collective and collaborative community resilience in responses to a variety of crises and exposed vulnerabilities in the COVID-19 era and beyond. With additional validation, this research will provide foundation to assist the federal and state governments, corporations, societal leaders to develop and implement strategies that will guide local and regional communities, and the nation into a successful new normal future.This exploratory yet transformative high risk-high payoff work that involves radically different approaches will have three main research components. First, the research team will construct a novel attributed heterogeneous information network (AHIN) to comprehensively model the up-to-date multi-source pandemic related data for abstract representation. Second, to understand how users interact and how information are propagated within and cross-community in social media, the team will develop an innovative nonnegative matrix factorization regularized deep graph learning model for community detection in the AHIN by considering the heterogeneity of the network. Third, the team will propose an integrated adversarial disentangler to separate the distinct, informative factors of variations hidden in the milieu to learn post embeddings for emotion and topic analysis for community classification and framing, and thus to derive supportive and constructive information for community resilience improvement. The developed AI-driven paradigm in this project will provide in-depth insights and customized guidance that can help public health experts, social workers, law enforcement, economists, and policy makers in decision-making and also enable a conceptual framework for the development of resilient community engagement strategies in responses to a variety of crises created by COVID-19 and future natural or health-related disasters. The research will be beneficial to multidisciplinary areas, including data mining, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities.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": "1363", "attributes": { "award_id": "2041065", "title": "EAGER: Collaborative Research: Understanding Human Behaviors and Mental Health using Federated Machine Learning on Smart Phones", "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": [ "7364", "7916" ], "program_officials": [ { "id": 3513, "first_name": "Wei", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-02-28", "award_amount": 75000, "principal_investigator": { "id": 3515, "first_name": "Ruoming", "last_name": "Jin", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 3514, "first_name": "Deric R", "last_name": "Kenne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 547, "ror": "https://ror.org/049pfb863", "name": "Kent State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding human behaviors and mental health are becoming increasingly important for modern society. The ongoing outbreak of coronavirus (COVID-19) not only further highlights its importance but also calls for immediate action. This project will develop a federated machine-learning (FL) framework and application on mobile device for understanding human behaviors and mental health. The planned research will synergize interdisciplinary research and particularly push the envelopes of federated learning and public health. This project will not only provide an important and timely real-world application, health-behavior monitoring and prediction, for the federated learning community, but also will advance our understanding of physical and mental health through mobile devices, and the impacts of COVID-19 to human society in a unique and detailed angle. This project will integrate the interdisciplinary research results into courses, and train students from underrepresented groups. Technically, the project has two main components: 1) Data collection and statistical analysis, and 2) Building federated learning framework and application. In the first component, the project will collect smartphone-based sensor data from student sub-population in both urban and suburban areas along with other health related surveys and data. The project will specifically analyze and determine what data collected from the mobile phone can be the indictors and causal factors of behavior and mental health. In the second component, the project will develop deep learning models to predict human behaviors, physical and mental health conditions/trends over time, under rigorous privacy protection. Specifically, the prediction models will be developed in federated learning settings to train the model locally on the device with differential privacy guarantees, without collecting sensor data to the cloud. Finally, the project will develop a federated learning based behavior monitoring and prediction application on mobile phones and will evaluate the prototype system on the cohort of studies from first component.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": "1620", "attributes": { "award_id": "2040273", "title": "RAPID: Antimicrobial Coatings for the mitigation of virus transmission on high-touch surface", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [ "7237", "7696", "7914" ], "program_officials": [ { "id": 4247, "first_name": "Steve", "last_name": "Smith", "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": 199954, "principal_investigator": { "id": 4250, "first_name": "Stephen J", "last_name": "McDonnell", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4248, "first_name": "John R", "last_name": "Scully", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4249, "first_name": "Daniel A", "last_name": "Engel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Non-technical AbstractThe goal of the proposed work is to limit the transmission of the COVID-19 virus and influenza virus through the development of new antimicrobial materials. The work focuses on metal alloys that can be used to coat regularly touched surfaces. Such surfaces are a major contributor to the spread of a virus as they become contaminated by an infected individual and lay in wait to infect a healthy person without any direct contact between the two individuals. An effective antimicrobial coating can dramatically reduce the length of time a virus can survive on such surfaces through release of oxidized copper and in that way minimize the likelihood of transmission. In this work, the focus is on balancing the antimicrobial behavior of the materials with the other critical aspects of a coating, specifically, the corrosion and passivation behavior, the surface preparation, and the impact of cleaning. Surface preparation and cleaning are important factors since it is vital that any coating be robust and effective in real-world environments. By obtaining a thorough understanding of the corrosion behavior, it becomes possible to better understand the science underlying the antimicrobial behavior and enables the future development of coatings either targeted to other viruses or suitably broad-spectrum to address a wide range of viruses so that the threat of future pandemics can be addressed by this strategy.Technical AbstractThe goal of this work is to determine the efficacy of antimicrobial functional copper-based alloys deployed as coatings in reducing the survivability of bacteria and viruses on high-touch surfaces. The process of mitigating virus viability is dependent on the concentration of copper ions released, the rate of release from the alloys as well as oxygen radicals produced as a result of electrochemical reactions, and the nature, concentration, and inoculum of the virus. These reactions and the fate of copper depend on alloy composition and the details of surface structure, morphology, and the nature of the passive oxide surface film produced by pretreatments and ambient exposure. A given copper cation concentration reduces virus viability depending on environmental factors and virus attributes. Hence, in this work virus viability is investigated using a palette of viruses representative of SARS-CoV-2 as well as influenza strains. Using a judicial selection of copper-based alloys and expected surface treatments as the starting point, the antimicrobial performance of these alloys, judged by virus unit death over time of exposure will be determined as a function of alloy composition and surface character produced by surface preparation. Detailed surface and electrolyte characterization is carried out to elucidate the details of the corrosion mechanism, which enable virus mitigation. This work seeks to enable the immediate selection of specific copper alloys for deployment as high-touch surfaces by answering the question of optimal alloy composition and treatment as well as affect future scientific principles of alloys further optimized for this function.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": "1542", "attributes": { "award_id": "2026944", "title": "RAPID: On-mask Chemical Modulation of Respiratory Droplets", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [ "7237", "7573", "7914", "8614" ], "program_officials": [ { "id": 4025, "first_name": "Birgit", "last_name": "Schwenzer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-01", "end_date": "2021-07-31", "award_amount": 200000, "principal_investigator": { "id": 4026, "first_name": "Jiaxing", "last_name": "Huang", "orcid": "https://orcid.org/0000-0001-9176-8901", "emails": "[email protected]", "private_emails": "", "keywords": "['Materials Chemistry and Processing']", "approved": true, "websites": "['http://sites.northwestern.edu/jxhuang/', 'http://sites.northwestern.edu/jxhuang/']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 317, "ror": "https://ror.org/000e0be47", "name": "Northwestern University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "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": "NON-TECHNICAL ABSTRACT: Spread of infectious respiratory diseases, such as influenza, SARS, MERS, and COVID-19, usually starts from virion-laden respiratory droplets, which are released by an infected person during coughing or sneezing. Most of the droplets end up depositing on various surfaces such as doorknobs, tabletops, buttons, handrails, and touchscreens, turning them into potentially infectious objects. For infection to occur, these virions must remain active when they are picked up by another person, often through direct contact by hands, and then transferred to mouth, nose and eyes. Direct transport of virus-laden droplets and nuclei to the respiratory tract is also possible through inhaling within close proximity to the source. Therefore, to slow down or even prevent virus spread, it would be desirable to greatly reduce the number and activity of the virions in those just-released respiratory droplets. This RAPID award, which is supported by the Solid State and Materials Chemistry Program in the Division of Materials Research, explores chemical modulation strategies for deactivating virions in the respiratory fluid droplets passing through a medical mask. This can reduce the number of active virions at the very source of their spread pathways, the cough. The project also helps to seed an effort to rally researchers in physical sciences and engineering to study the problems, develop new hypotheses, create user-centered solutions and educate the general public, to address the many challenges associated with the transmission and spread of infectious respiratory diseases.TECHNICAL ABSTRACT: Facial masks are often required for patients to block and absorb large respiratory fluid droplets, and to reroute those smaller escaping droplets to reduce their forward travelling distance. It would be desirable to develop drop-in strategies to add anti-viral functions to the disposable masks used by patients. This RAPID project, which is supported by the Solid State and Materials Chemistry Program in the Division of Materials Research, develops such an on-mask, chemical modulation strategy that focuses on altering the chemical composition of the escaped respiratory droplets to deactivate virions. The starting model system is a chemical modifier based on a conducting polymer. The polymer is doped with chemical agents that are known to generate harsh micro-environment to deactivate virions. The dopants can readily dissolve in warm respiratory fluid droplets during exhalation, but they do not vaporize in the incoming stream of colder and drier air during inhalation. Such an on-mask chemical modulation strategy adds chemical sanitization function to common medical masks for reducing the viability of virions. Since this drop-in chemical modulation is applied at the very beginning of the chain events of virus transmission, it is effective for all potential transmission pathways. Additionally, through this award stronger connections between biological/medical research and physical sciences/engineering are established and serve to inspire new hypotheses, questions and ideas that drive innovations to address the challenges associated with the transmission and spread of infectious respiratory diseases. A significant effort of this RAPID project is used to achieve this goal, so that the physical sciences and engineering communities can be better informed, educated and prepared to work with biological and medical researchers to create solutions, and join them in the educational outreach activities for the general public.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": "1528", "attributes": { "award_id": "2037297", "title": "RAPID: Virtual Reality Cyberinfrastructure Enabling Data Visualization, Exploration, and Collaboration Among Distant Individuals", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [ "1206", "7914" ], "program_officials": [ { "id": 3986, "first_name": "Hans", "last_name": "Krimm", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2023-02-28", "award_amount": 66833, "principal_investigator": { "id": 3987, "first_name": "Danny", "last_name": "Milisavljevic", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has introduced obstacles to many areas of astronomical research, especially topics requiring access to shared facilities and driven by interaction between scientists to interpret complex multi-dimensional data. A research team at Purdue University will accelerate production of the Virtual Reality (VR) data visualization platform Collaborative Astronomy VR, which leverages recent advancements in high-powered consumer hardware and cloud-based multi-user server architecture. Building this platform will globally connect individuals isolated by COVID-19 restrictions and provide virtual environments where they can actively engage with complex data and representations. The distributed application will be tailored for immersive visualization of supernova explosions and other topics in astronomy, but will have flexibility for potential use in other fields requiring visualization and interaction with 3D data environments. The work will contribute to a graduate student thesis and provide opportunities for undergraduate students, who will actively help develop and showcase the technology. A small instructional lab for physics and astronomy classes will also be created.The platform will be appropriate to visualize, explore, and collaborate with data made from observations and simulations of astronomical objects and phenomena. The flexible data ingestion pipeline will support multi-dimensional formats common to astronomical data, and will have clean up and optimization procedures to ensure data fidelity and fluid visualizations. This project fulfills the urgent need for scientists to navigate complex, multi-dimensional data collaboratively with distant individuals via scalable multiuser connectivity and broad platform compatibility. Collaborative Astronomy VR enables rapid and intuitive exploration of multidimensional data with on-the-fly scaling, rotation, and changing of perspective among many participants connected online simultaneously.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": "1447", "attributes": { "award_id": "2032802", "title": "EAGER: Investigating the rapid transition from face-to-face to exclusively online engineering laboratory classes in an Electrical and Computer Engineering program", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "110E", "1340", "7916", "8045" ], "program_officials": [ { "id": 3754, "first_name": "Jumoke", "last_name": "Ladeji-Osias", "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": 300000, "principal_investigator": { "id": 3759, "first_name": "Dominik", "last_name": "May", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 160, "ror": "", "name": "University of Georgia Research Foundation Inc", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3755, "first_name": "Fred R Beyette", "last_name": "Jr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3756, "first_name": "Beshoy", "last_name": "Morkos", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3757, "first_name": "Nathaniel", "last_name": "Hunsu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3758, "first_name": "Andrew M", "last_name": "Jackson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 160, "ror": "", "name": "University of Georgia Research Foundation Inc", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 global health crisis has forced educators to rapidly shift to online instruction methods. While this effort is already difficult for faculty, it is particularly challenging for instructors who have designed a portion of their curricula with hands-on laboratory instruction, as in the case for many STEM courses. To offer the effective education in-person instruction affords, it is important to maintain the same educational and curricular value of laboratories while taking advantage of benefits that online education and online experimentation tools offer. This project will transcend the demands of the COVID-19 crisis as it investigates the impact of using a full set of online laboratory modules in an engineering laboratory course. Impacts on faculty resistance to adoption, student motivation and self-regulation, and user experience and success are evaluated in this study. Online labs (remote and virtual) have long been considered promising options to enhance the student educational experience, but broad adoption has been limited. The current circumstances allow us to establish a successful use case by re-casting a semester-long, hands-on, experiential learning component into a fully online set of instructional labs. Nevertheless, rapidly deploying fully online experimentation and instruction in engineering challenges our dominant instructional model, especially in a traditional face-to-face institution in which both faculty and students may show resistance but also surprising adoption of online education technology. While associated with considerable challenges, establishing and empirically investigating such a successful use case offers high reward opportunities for knowledge generation, as well as benefits for faculty and students in engineering programs across the nation.This project will leverage the development and deployment of online labs and integrated online instruction modules to investigate the impact of using an exclusively online instructional mode for a fundamental electrical and computer engineering laboratory course. To reach this goal, the project will answer the following three research questions: A) How do faculty experience a top-down mandated, time-constrained, and rapid transition to exclusively online-based laboratory modules in engineering courses along the continuum of resistance towards the wholesale embrace of educational technologies? B) How does exclusively online laboratory instruction and online experimentation impact students’ learning experiences in terms of engagement, investigated through self-regulation and motivation? C) What user experience factors influence the success of introducing exclusively online experimentation activities into engineering courses and curricula? The research design will comprise three thrusts: faculty, student, and user experience perspective. All thrusts will employ a convergent, parallel, mixed-methods research approach using quantitative and qualitative measures. Informed by theoretical frameworks including the diffusion of innovation, the propagation paradigm, student cognitive and emotional engagement, and user-centered design, this project will develop in-depth knowledge on how online experimentation can be successfully deployed in engineering education settings. As a result, this project will a) lead to a framework for educational technology propagation, specific to online engineering labs and reflecting factors for both efficacies and fit, b) inform a model for student-centered online experimentation with explicit guidelines for student support, and c) inform a comprehensive model of success factors for designing user-centered online experimentation activities. The results of this research will also expand the reach and impact of engineering disciplines that have historically required face-to-face facilities to provide integral laboratory instruction. This project will enable online experimentation experiences that support core education learning objectives while also meeting the challenges of a diverse, mobile, and geographically distributed engineering workforce.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": "1494", "attributes": { "award_id": "2034030", "title": "RAPID: Building Research Capabilities of Academically Talented Undergraduate Students from Hispanic-Serving Institutions through Virtual Research Experiences", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [ "097Z", "7914", "8817" ], "program_officials": [ { "id": 3887, "first_name": "Erika Tatiana", "last_name": "Camacho", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2024-07-31", "award_amount": 443929, "principal_investigator": { "id": 3889, "first_name": "Ann Q", "last_name": "Gates", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 446, "ror": "", "name": "University of Texas at El Paso", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3888, "first_name": "Elsa Q", "last_name": "Villa", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 446, "ror": "", "name": "University of Texas at El Paso", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The Improving Undergraduate STEM Education: Hispanic-Serving Institutions Program (HSI Program) supports RAPID projects when there is severe urgency with regard to availability of, or access to, data, facilities or specialized equipment, including quick-response research on natural or anthropogenic disasters and similar unanticipated events, such as the COVID-19 pandemic. This project aims to develop effective virtual internships and undergraduate research experiences to expand learning and professional development opportunities in computer sciences to minoritized undergraduate students. The COVID-19 pandemic caused a disruption in society across all sectors with widespread loss of internships, jobs, and undergraduate research experiences that negatively impact the talent pipeline in computing fields and the future of U.S. innovation. Such disruptions are likely to occur in the near future, and the U.S. must be well-prepared in offering effective virtual development opportunities and experiences for students. This project will address this exigency by refining a proven model for developing professional, research, team, and communication skills so that it will be effective in a virtual environment.The project will engage students from diverse backgrounds and with diverse experiences in impactful research projects while proving students funding that will, in part, support continuation of their education. Broadening participation of minoritized students in research can increase retention and graduation rates in computing fields. The proposed project will provide faculty professional development in the Affinity Research Group (ARG) model, a Computing Alliance of Hispanic-Serving Institutions (CAHSI) signature and proven practice for immersing students in research. Faculty will also learn how to incorporate ARG elements into classroom pedagogical practice to improve teaching and learning. The effort will lead to a national community of faculty who are skilled at undergraduate research mentoring informed by the ARG model and who grow through their involvement with a cohort of researchers and participation in ARG interactive webinars. The evaluation will identify the practices that work best for the virtual Research Experiences for Undergraduates (REUs). It will also examine whether student perceptions of computer science and their career goals change after participation in a virtual REU. In addition, the evaluation will investigate the benefit to faculty of joining a cohort community in support of research with undergraduate. The development of a model for effective virtual undergraduate research experiences can benefit many students, especially students who do not have the ability to travel to remote REUs because of family obligations. This RAPID award is made by the Hispanic-Serving Institutions Program in the Division of Human Resource Development, Directorate of Education and Human Resources. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs.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": 4, "pages": 1397, "count": 13961 } } }