Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=-principal_investigator" }, "data": [ { "type": "Grant", "id": "573", "attributes": { "award_id": "2024033", "title": "NSF Student Travel Grant for 2020 IEEE Radio Frequency Integrated Circuits Symposium (RFIC 2020). To be held August 4-6, 2020.", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1231, "first_name": "Donald", "last_name": "Wunsch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2021-08-31", "award_amount": 10000, "principal_investigator": { "id": 1232, "first_name": "Donald D", "last_name": "Lie", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 270, "ror": "https://ror.org/0405mnx93", "name": "Texas Tech University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 270, "ror": "https://ror.org/0405mnx93", "name": "Texas Tech University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project helps support the participation of around 40 US-based students to attend the IEEE Radio Frequency Integrated Circuits Symposium (RFIC 2020) originally scheduled for June 21-23, 2020 in Los Angeles and now a virtual symposium due to the COVID-19 pandemic. The Symposium is part of Microwave Week 2020, the world’s largest RF & microwave technical convention and will now be held from August 4-6, 2020. Over the last three years, the RFIC Symposium has been a three-day long event with over 900 registered attendees, including about 300 students. The project will support the selected students that are chosen for their registration fees to the RFIC Symposium. The RFIC Symposium organizers seek to increase the number of student participants by targeting women, undergraduates, and under-represented groups for participation. The aim is to support young researchers who might not otherwise be able to attend RFIC 2020 due to financial difficulty as well as to encourage the diversity of the field.The RFIC Symposium is one of the world’s premier events to learn about RFIC design, technology and research. Students are able to obtain feedback on their work (through paper presentations and demos and competition), talk with senior researchers from government, industry, and academia, and get exposed to the latest research. RFIC 2020 has a number of events specifically beneficial to student researchers, which are technical talks, interactive demo session, workshops, tutorials, technical lecture, exhibition, the three-minute thesis (3MT®) competition program, and other targeted student activities. To summarize, participation at RFIC 2020 will expose these US-based students to this critical research field of US national interests (e.g., 5G wireless), enhance the research experiences for students, and provide increased opportunities for new collaborations to advance knowledge.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": "572", "attributes": { "award_id": "2037101", "title": "FMRG: Adaptable and Scalable Robot Teleoperation for Human-in-the-Loop Assembly", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1225, "first_name": "Bruce", "last_name": "Kramer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2025-12-31", "award_amount": 3749150, "principal_investigator": { "id": 1230, "first_name": "Shuran", "last_name": "Song", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "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 } ] }, "other_investigators": [ { "id": 1226, "first_name": "Steven K", "last_name": "Feiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1227, "first_name": "Matei T", "last_name": "Ciocarlie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1228, "first_name": "Chandana", "last_name": "Mahadeswaraswamy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1229, "first_name": "Tristan", "last_name": "Bel", "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": "The COVID-19 pandemic has accelerated the adoption of remote working in many industries. The ability for employees to work remotely, often from home, has become crucial to an organization's long-term resilience and growth potential. However, while advances in software and networking have made it possible for information workers to work remotely, most manufacturing workers cannot, because the infrastructure that is needed doesn't exist. This Future Manufacturing (FM) project will research an adaptable and scalable robot teleoperation system that allows factory workers to work remotely. The research will benefit both the manufacturing industry and the workforce by increasing access to manufacturing employment and improving working conditions and safety. By combining human-in-the-loop design with machine learning, this research can broaden the adoption of automation in manufacturing to new tasks. Beyond manufacturing, the research will also lower the entry barrier to using robotic systems for a wide range of real-world applications, such as assistive and service robots. The research team is collaborating with NYDesigns and LaGuardia Community College to translate research results to industrial partners and develop training programs to educate and prepare the future manufacturing workforce.This research suggests three key ideas to enable human-in-the-loop assembly: First, the system uses a physical scene understanding algorithm that converts the real-world robot workspace into a virtual manipulable three-dimensional scene representation. Next, a three-dimensional Virtual Reality user interface will be used to allow users to specify high-level task goals using this scene representation. Finally, the system uses a goal-driven reinforcement learning algorithm to infer an effective planning policy, given the task goals and the robot configuration. This system can overcome several limitations of existing teleoperation systems. By separating high-level task planning from low-level robot control using a physical scene representation, the system allows the operator to specify task goals without having expert knowledge of the robot hardware and configuration. By using reinforcement learning for low-level control, the system is more generalizable to new tasks and hardware.This award is co-funded by the Divisions of Civil Mechanical and Manufacturing Innovation, Electrical, Communications and Cyber Systems, Computer and Network Systems, Undergraduate Education, and Behavioral and Cognitive Sciences and the Cyber Physical Systems, NSF Scholarships in Science, Technology, Engineering, and Mathematics, and Advanced Technological Education Programs.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": "571", "attributes": { "award_id": "2031920", "title": "Collaborative Research: Equity of Access to Computer Science: Factors Impacting the Characteristics and Success of Undergraduate CS Majors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 1223, "first_name": "Alexandra", "last_name": "Medina-Borja", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-15", "end_date": "2023-11-30", "award_amount": 348021, "principal_investigator": { "id": 1224, "first_name": "Christine J", "last_name": "Alvarado", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by improving undergraduate computer science education. It will do so by completing a research study that can reveal potential systemic limitations in access to computer science education by all students. This research study will examine ten-years of undergraduate student application, admissions, and retention data from four institutions. Analysis of these data will describe how students of varying demographics and pre-college preparation are present throughout the computer science talent pipeline. This study will fill an important research gap about factors that affect the flow of students into and through the computer science major. It is well documented that the demographic characteristics of computer science students are highly skewed toward males versus females and have skewed racial/ethnic distributions. What is not yet understood is at what point in the talent pipeline these imbalances are greatest and the degree to which they change as students progress through computer science undergraduate programs. In addition, the current educational disruption caused by COVID-19 provides the important and unique opportunity to determine what effect, if any, the resulting educational changes have had on participation of different groups of students in computer science. Students from underrepresented groups appear to have encountered greater difficulty accessing distance learning and being connected to the full range of educational opportunities presented by these unique circumstances, which are very strongly related to technological know-how. There is legitimate cause for concern that the pandemic will further divide the advantaged from the disadvantaged, further marginalizing the underrepresented groups that the project is studying from opportunities to advance into computer science majors and progress successfully through them. Computer science is an area of critical strategic importance for the nation, and a field in which cultivating domestic talent can have enormous impact. Thus, examining pre- and post- pandemic patterns of participation in computer science have the potential to help the nation meet its growing needs for talent in computer science and related fields, such as cybersecurity and artificial intelligence. This study will use a large, rich data set compiled from ten years of undergraduate application, admissions, and course-level data from four institutions: Loyola Marymount University, Cal State University Long Beach, the University of California Riverside, and the University of California San Diego. Analysis of these longitudinal data will improve understanding of who has access, who applies, who is admitted, and who succeeds in computer science. Using classical statistical approaches and modern machine learning based approaches to analysis of large data sets, the study seeks to understand how to improve the inclusion of all students in computer science. It will supplement this large-scale quantitative analysis with qualitative analysis of results from targeted focus-groups and interviews. The qualitative analysis, coupled with the quantitative analysis of longitudinal data from four institutions with different student demographics and other characteristics, will provide a deeper analysis of access to and success in computer science than any previous study. The resulting extension of knowledge has the potential to lay the foundation for achieving equitable access to computer science education for all students. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "570", "attributes": { "award_id": "2038612", "title": "CPS: TTP Option: Medium: Discovering and Resolving Anomalies in Smart Cities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1217, "first_name": "Donald", "last_name": "Wunsch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 1216000, "principal_investigator": { "id": 1222, "first_name": "Srinivasa G", "last_name": "Narasimhan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1218, "first_name": "Christoph J", "last_name": "Mertz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1219, "first_name": "Artur", "last_name": "Dubrawski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1220, "first_name": "Stephen F", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, { "id": 1221, "first_name": "Robert", "last_name": "Tamburo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding complex activity due to humans and vehicles in a large environment like a city neighborhood or even an entire city is one of the main goals of smart cities. The activities are heterogeneous, distributed, vary over time and mutually interact in many ways, making them hard to capture and understand and mitigate issues in a timely manner. While there has been tremendous progress in capturing aggregate statistics that helps in traffic and city management as well as personal planning and scheduling, much of this work ignores anomalous patterns. Examples include protests, erratic driving, near accidents, construction zone activity, and numerous others. Discovering and resolving anomalies is challenging for many reasons as they are complex and rare, depend on the context and depend on the spatial and temporal extent over which they are observed. There are potentially a large number of anomalies or anomalous patterns, so they are impossible to label and describe manually. The PIs will conduct research to address automatic discovery and resolution of anomalous patterns in smart city visual data. The PIs will leverage the large amount of visual data they have access to ranging from cameras at many intersections in Pittsburgh, around the Carnegie Mellon University neighborhood, cameras installed on public buses, and physical distribution networks in the city. The project will include the following four closely integrated research thrusts: (1) Extracting anomalies in the presence of noise due to visual processing algorithms, (2) Automatically discover anomalies at different spatial and temporal scales with intelligent coordinated and distributed planning, (3) discovering the relationship with anomalies and context, and (4) Resolving Anomalies through Hard and Soft Actuation using both automatic and human-in-the-loop methods. The work will enable the following applications: Safer and more efficient roads, monitoring the roadway infrastructure and roadside, maximizing the distribution services and informing decisions on health policy (including COVID-19). The project will be conducted in collaboration with several stakeholders - multiple infrastructure and traffic management startups and local city government - in a comprehensive transition to practice program designed to deploy the research in the real world.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": "569", "attributes": { "award_id": "2041968", "title": "I-Corps: Air Quality Monitoring Based on Digital Inline Holography", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1215, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2023-01-31", "award_amount": 50000, "principal_investigator": { "id": 1216, "first_name": "Lei", "last_name": "Feng", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "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 broader impact/commercial potential of this I-Corps project is to define the requirements for the development of a new generation of indoor air quality monitors based on digital inline holography (DIH). The physical pollutant monitoring segment in the global air quality monitoring market is around $1 billion and is projected to grow rapidly amid and after the COVID-19 pandemic. The proposed DIH monitor may identify pollutant types in addition to pollutant counts and size distribution. The proposed DIH sensor also has a compact design that may be used as wearable/portable devices or that may be easily integrated into filtration systems both in home and automobiles for smart control. The proposed technology may be further expanded for monitoring and characterization of particles in a broad range of applications, including monitoring: outdoor airborne particles, particles suspended in water, particle characterization in atomization and sprays, particle processing, and high throughput cellular analysis in biomedical applications.This I-Corps project explores the translation of machine learning-based digital inline holography (DIH). A highly compact 3D imaging sensor may perform real-time, in situ measurements of size distributions, shape, concentration, and other physical properties of particles (e.g., droplets, bubbles, dusts, cells, etc.) from 200 nm to ~1 mm. The proposed technology may serve as a platform technology that may be applied to monitor air quality. The DIH sensor may be used to monitor a wide range of airborne pollutants including physical pollutants (e.g., smoke, smog, dust, etc.) and biological pollutants (e.g., pollen, dust mites, house dust, animal dander, etc.)distinguishing particulate pollutants types based on particle shapes. At the conclusion of the project, the goal is to identify the requirements for air pollutant monitoring applications and the need for this technology in the broader particle diagnostic community.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": "568", "attributes": { "award_id": "2037359", "title": "Workshop: Establishing the Vision and Creating a Roadmap for Security, Privacy and Ethics Research in Healthcare", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1213, "first_name": "James", "last_name": "Joshi", "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": 87972, "principal_investigator": { "id": 1214, "first_name": "Jaideep", "last_name": "Vaidya", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 303, "ror": "", "name": "Rutgers University Newark", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 303, "ror": "", "name": "Rutgers University Newark", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Healthcare is a societal need with health spending accounting for 17.7% of the Gross Domestic Product (GDP) today. There are new challenges such as new and evolving diseases (e.g., antibiotic resistant bacteria), evolving situations (e.g., COVID-19 pandemic), evolving behavior (e.g., anti-vaxxer movement, sedentary lifestyle, opioid crises) that require constant innovation in therapeutic treatments, drug discovery, and other aspects. Advances in information and sensing technologies, communication platforms, and robotics assisted systems, have led to significant improvements in the quality of healthcare delivery and in controlling the costs of healthcare. Within the Health and Human Services sector, there is rapid adoption of new technologies such as IoT devices, robotics assisted systems, mobile and Edge/Cloud computing, Social Networks, AI/ML, etc., creating a data-intensive hyper-connected cyber-physical interacting systems. These new technologies provide increasing opportunities to realize the dream of personalized health and well-being. However, all of these technologies bring a plethora of their own unique security, privacy and ethical challenges.The objective of this workshop is to bring together approximately 50-60 scientists, students, and stakeholders to identify the unique challenges in terms of security, privacy, fairness, and ethics underlying the use of information technology and computing in healthcare. By bringing together participants from computing, informatics, and healthcare, across academia, industry, and government the workshop aims to enable cross-fertilization between these communities, and the identification of the major challenges that are unique to this context. The insights and findings that results will be summarized in a workshop report and disseminated to the attendees and scientific experts in the fields of security, privacy, healthcare, and informatics.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": "567", "attributes": { "award_id": "2030482", "title": "SBIR Phase I (COVID-19): Identifying Medical Supply Shortages on Social Media for Fast and Effective Disaster Response", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 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": "2020-08-01", "end_date": "2022-07-31", "award_amount": 255207, "principal_investigator": { "id": 1212, "first_name": "Spencer", "last_name": "Vagg", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 302, "ror": "", "name": "INITIUM AI INC.", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 302, "ror": "", "name": "INITIUM AI INC.", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project consists of providing immediate help during the COVID-19 crisis by identifying the needs of medical providers and compiling reports for government agencies and medical equipment suppliers and manufacturers. The proposed Natural Language Processing methodology will help (1) hospitals and clinics seeking medical supplies, personal protective equipment, and testing supplies to meet their needs; (2) the government coordinating response; (3) manufacturers and suppliers seeking information regarding needs. Additionally, it can be used to identify other non-medical supply shortages and can be adapted to provide an efficient response for other disasters or outbreaks.This Small Business Innovation Research (SBIR) Phase I project will leverage recent advances in natural language processing and machine learning to identify at scale needs in medical equipment and supplies, based on insights derived from free text in social media, and convert these needs into a centralized, easily accessible structured data format. The technology will identify expressions of needs on social media; identify users, their specific needs, and locations; and generate geographically sorted actionable formatted lists.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": "566", "attributes": { "award_id": "2037137", "title": "Build and Broaden: Advancing fundamental knowledge of social, behavioral, and economic responses to pandemics in minority communities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1209, "first_name": "Jan", "last_name": "Leighley", "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": 49913, "principal_investigator": { "id": 1210, "first_name": "Julie M", "last_name": "Mueller", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 301, "ror": "https://ror.org/0272j5188", "name": "Northern Arizona University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 301, "ror": "https://ror.org/0272j5188", "name": "Northern Arizona University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 provides an opportunity for Social, Behavioral and Economic science (SBE) researchers to develop and test new theories regarding sociocultural differences in responses to pandemics. Diverse perspectives in SBE research are essential to develop outcomes that benefit all members of society and promote the progress of science. Faculty at Minority Serving Institutions (MSI)’s have a unique perspective on COVID-19 and the potential to make significant contributions to fundamental research regarding minority community responses to pandemics. Northern Arizona University (NAU) has twenty statewide campuses, including the main Flagstaff mountain campus, and NAU-Yuma. NAU-Yuma is an MSI with 74% of students identifying as Hispanic/Latino. The NSF award will support a workshop at NAU-Flagstaff to advance fundamental knowledge of the social, behavioral and economic responses to pandemics in minority communities. The goal of the workshop is to build capacity at NAU-Flagstaff and NAU-Yuma for SBE researchers to obtain competitive grant funding and broaden collaborations with faculty from an MSI in SBE research. The workshop will span two days and convene internationally recognized experts in behavioral and regional economics, diverse and early-career researchers in social, behavioral and economic sciences, and grant-writing specialists. The collaborative workshop format includes presentations from keynote speakers, expert led panels and small group breakout sessions. Participants will address two fundamental research questions: 1) What sociocultural factors determine behavioral responses to pandemics? and 2) How do changes in post-pandemic preferences impact tourism-dependent economies? The project deliverables include development of testable hypotheses that incorporate innovative research ideas with a diverse perspective and submission of competitive research proposals with MSI faculty collaborators.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": "565", "attributes": { "award_id": "2035359", "title": "NSF CONVERGENCE ACCELERATOR: Improving Online Education Through Technology, Research, And Data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director" ], "program_reference_codes": [], "program_officials": [ { "id": 1206, "first_name": "Mike", "last_name": "Pozmantier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2021-08-31", "award_amount": 99220, "principal_investigator": { "id": 1208, "first_name": "Scott", "last_name": "Crossley", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 300, "ror": "", "name": "Georgia State University Research Foundation, Inc.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1207, "first_name": "Kathryn S", "last_name": "McCarthy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 300, "ror": "", "name": "Georgia State University Research Foundation, Inc.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has exponentially accelerated the growth of online educational technology. Digital platforms are seeing a 10 fold increase in users. Still, many current educational technologies have weak approaches to instruction, and they do not leverage basic research on learning, have a culture of continuous improvement, meet the needs of diverse learners, or leverage the growing power of computers. This conference will provide ideas for deliverables around potential use-cases and prototypes as well as lay the groundwork for future collaborations across a variety of disciplines to research, develop, and refine effective and equitable remote educational technologies.This project will bring together experts in industry and fields such as education, cognitive science, and technology to identify barriers and solutions to delivering high-quality online education. These collaborations will inform best practices in educational technology design and generate future development and testing. Topics such leveraging AI/ML or new modes of platform design will be key components of the discussions that take place at this conference, along with a look at what types deliverables can be derived from applying these new approaches to online learning.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": "564", "attributes": { "award_id": "2015112", "title": "SBIR Phase I (COVID-19): Developing a comprehensive and customizable science courseware grounded in evidence-based teaching and learning practices", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1204, "first_name": "Diane", "last_name": "Hickey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-01-31", "award_amount": 224960, "principal_investigator": { "id": 1205, "first_name": "Ashley A", "last_name": "Rowland", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 299, "ror": "", "name": "CODON LEARNING, INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 299, "ror": "", "name": "CODON LEARNING, INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance the state of practice of STEM courses. Certain techniques, known as evidence-based teaching (EBT), can improve performance for all STEM learners and narrow the achievement gaps, but 80% of STEM classrooms are still primarily lecture-based. This project will develop a digital course design tool enabling an instructor to quickly create and implement an exciting STEM course course. It will create and scale a plug-and-play library of high-quality assessment items and instructional resources in a way that instructors find empowering, easy to use, and valuable. This will be valuable during a period of remote learning, such as that created by the social distancing of the COVID-19 situation. This Small Business Innovation Research (SBIR) Phase I project will support the development and testing of a system to distribute EBT course structure and content at scale. Active learning and high-structure courses produce better outcomes, and therefore this project focuses on dissemination of EBT curricula and course structures. The research objectives are to automate course design, and explore user requirements for the system to scale, and produce real-time feedback on student performance.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": 1391, "pages": 1419, "count": 14184 } } }