Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1397&sort=-principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1398&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1396&sort=-principal_investigator" }, "data": [ { "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 } }, { "type": "Grant", "id": "563", "attributes": { "award_id": "2032258", "title": "Collaborative Research: A comprehensive approach to modeling, learning, analysis and control of epidemic processes over time-varying and multi-layer networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1202, "first_name": "Lawrence", "last_name": "Goldberg", "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": 199998, "principal_investigator": { "id": 1203, "first_name": "Philip E", "last_name": "Pare", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Numerous natural and engineered systems consist of underlying networked components over which dynamical processes evolve. Examples include the spread of infectious disease processes over human contact and international travel networks, the propagation of peak traffic phenomenon over the transportation infrastructure, the spread of viruses or worms over computer networks, and sharing and re-sharing of posted articles, tweets, or rumors over social media platforms. Engineered systems are increasingly interconnected over various levels of both local and large-scale networks. Developing a stronger understanding at fundamental and analytical levels of how viral processes evolve across different network structures, the rates at which they spread, how the occurrence of multiple viral types and multiple network layers affect the spread process dynamics, and how these processes can be suppressed and/or mitigated by employing deliberate control policies will greatly impact the health, safety and security of a vast variety of systems around the globe. The spread of COVID-19 has clearly had broad implications for the health of people on all six inhabited continents as well as the world's economy. The research proposed herein will substantially enhance our understanding of epidemics such as COVID-19 and lead to general policy guidelines that will help limit the loss of human life and reduce the economic impacts of the virus. The methods to be developed in this project will be beneficial for battling subsequent epidemic outbreaks, a second wave of COVID-19, and on a broader scale general viral process. Throughout this project the PIs will build on their past experiences to make every effort towards recruiting and mentoring students from under-represented groups, and will establish outreach efforts by including undergraduate and local high school student researchers.Although the dynamics of epidemic processes over networks have been extensively studied for the past 10-15 years, past work has been focused largely on SIS and SIR processes spreading over static networks. In the proposed project, our focus will be on modeling, analysis and control of dynamic epidemic processes over large and possibly time-varying networks, comprised of multiple layers at multiple scales. We will specifically consider data-informed modeling and analysis of SAIRS (susceptible-asymptomatic-infected-recovered-susceptible) processes over time-varying networks; this work will include stability and equilibria analysis of the nonlinear dynamics of networked epidemic process models, network structure identification, estimation of parameters and structure from imperfect and non-random data, and development of realizable control strategies from the agent level to societal levels. The research proposed will draw on and contribute to wide-ranging foundational results in mathematical modeling and analysis of infectious diseases, time-varying nonlinear and linear analysis methods, optimization and control-theoretic policy formulation, network inference and analysis, sequential sampling strategies with stochastic sample constraints, and mean-field games over networks.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": "562", "attributes": { "award_id": "2030272", "title": "Collaborative Research: SWIFT: LARGE: DYNAmmWIC: Dynamic mmWave Spectrum Sharing Techniques for Public Safety Communications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1198, "first_name": "Murat", "last_name": "Torlak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-12-31", "award_amount": 500000, "principal_investigator": { "id": 1201, "first_name": "Mehmet C", "last_name": "Vuran", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 298, "ror": "", "name": "University of Nebraska-Lincoln", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1199, "first_name": "Jennifer K", "last_name": "Ryan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1200, "first_name": "Demet", "last_name": "Batur", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 298, "ror": "", "name": "University of Nebraska-Lincoln", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true }, "abstract": "The ongoing COVID-19 has, once more, highlighted the importance of public safety communications (PSC) in protecting public safety personnel and ensuring life-saving services. As 5G networks transition to higher frequencies, including the mmWave spectrum, integration of PSC solutions into this new spectrum becomes a challenge. Increased data rates, reduced delays, and improved density would lead to the realization of life-critical PSC use cases such as high-quality real-time video streaming and ultra-low-delay communication. These use cases increase the potential demand for PSC in the mmWave spectrum. Conventional spectrum management solutions, such as allocating dedicated PSC bands, are not sustainable. This project develops dynamic mmWave spectrum sharing solutions for public safety communications (DYNAmmWIC) to allow for novel PSC use cases in the mmWave spectrum and design the mmWave spectrum. This vision requires the coexistence of innovations in the radio, network operation management, and network architecture levels. The developed solutions in this project have the potential to fundamentally transform mmWave spectrum usage and accelerate the readiness of the wireless industry for 6G solutions while saving significant costs. The project explores four main goals to provide a complete radar-5G-public safety communications (PSC) coexistence solution from radio hardware to the network architecture: (1) A joint-radar communication (JARC) system is developed by using the same waveform for both radar sensing and data transmission in commercial and public safety vehicles. (2) A mmWave spectrum sharing solution is developed to dynamically manage the coexistence of operations for PSC, radar sensing, 5G, and V2V communications, pedestrian access, and backhaul in the mmWave spectrum. (3) A novel mmWave next-generation radio access network (NG-RAN) architecture is developed, allowing higher RAN co-operation and integration through proven orchestration tools for rapid development iterations of solutions on a real testbed. (4) A comprehensive evaluation plan complements the developed techniques. The JARC system, implemented at OSU, will augment iLNK, a city-wide, remotely accessible wireless testbed. iLNK is developed at UNL in collaboration with the City of Lincoln, which has proven to be a valuable remote teaching tool during the COVID-19-related restrictions. PIs will work to roll-out educational modules that can be conducted on the remotely accessible iLNK testbed, lowering the barrier to wireless education. This project will be conducted by a highly diverse team of PIs with a long track record of productive collaboration and impacts on diversity. Proposed solutions are rigorously tested to demonstrate a next-generation PSC model to local and national stakeholders.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": "561", "attributes": { "award_id": "2041547", "title": "University-Industry Partnerships in the Social Sciences Helping Organizations Achieve Impact", "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": 1196, "first_name": "Tara", "last_name": "Behrend", "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": 58548, "principal_investigator": { "id": 1197, "first_name": "Ted", "last_name": "Knight", "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": "We live in a complex social world in which the challenges facing society evolve continuously, and organizations must adapt to meet them. With the rapid growth of social networks over the last two decades and the corresponding availability of big data, the behavioral and social sciences have become increasingly important to the development and growth of organizations’ capacity to understand and address global challenges. The recent COVID-19 pandemic has accentuated the importance of the social sciences in providing guidance during societal crises, particularly with respect to the successful operation of organizations during emergencies. Social, behavioral, and organizational science can help organizations address societal needs, and these contributions can be magnified through collaborations between academia and industry. With respect to such issues as shaping the future of work, harnessing data for societal benefit, supporting diversity and inclusion, advancing team science, establishing successful leadership and mentorship models within organizations, and understanding the impact of artificial intelligence on society, the social sciences are critical for advances and solutions. There are significant opportunities for expanding academic-corporate partnerships in the social sciences to help address urgent societal needs. The University of Maryland, College Park, in collaboration with the University Industry Demonstration Partnership, will host a three-day workshop in College Park, Maryland, convening a diverse group of experts and leaders from academia, industry, and government to consider how academic-corporate partnerships can advance social, behavioral, and organizational science research to positively impact science and society. This workshop will help generate more awareness about the collaborative opportunities that exist within the social, behavioral, economic, and organizational science disciplines, and will produce strategies to fuel future industry-university partnerships. The workshop will showcase research collaborations relevant to business practitioners, policy makers, and research communities, examine how organizations develop and operate successfully, especially amid current challenges such as the COVID-19 pandemic, and explore how university-industry collaborations in the social sciences can stimulate discovery, knowledge exchange, and economic development, helping to create value and achieve social impact.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": "560", "attributes": { "award_id": "2037983", "title": "Online Practice Suite: Practice Spaces, Simulations and Virtual Reality Environments for Preservice Teachers to Learn to Facilitate Argumentation Discussions in Math and Science", "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": 1190, "first_name": "Michael", "last_name": "Steele", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2023-07-31", "award_amount": 2187740, "principal_investigator": { "id": 1195, "first_name": "Jamie N", "last_name": "Mikeska", "orcid": "https://orcid.org/0000-0002-8831-2572", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 296, "ror": "https://ror.org/03b5q4637", "name": "Educational Testing Service", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1191, "first_name": "Meredith Park", "last_name": "Rogers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1192, "first_name": "Pamela", "last_name": "Lottero-Perdue", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1193, "first_name": "Heather", "last_name": "Howell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1194, "first_name": "Justin F", "last_name": "Reich", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 296, "ror": "https://ror.org/03b5q4637", "name": "Educational Testing Service", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "In teacher education it is widely acknowledged that learning to teach requires that preservice teachers have robust, authentic, and consistent opportunities to engage in the work of teaching--ideally across different contexts, with diverse student populations, and for varied purposes--as they hone their instructional practice. Practice teaching experiences in K-12 classrooms, such as field placements and student teaching, are the most widely used approaches to provide these opportunities. In an ideal world these experiences are opportunities for preservice teachers to observe and work closely with mentor teachers and try out new instructional strategies with individual, small groups, and whole classes of K-12 students. While these experiences are critical to supporting preservice teachers’ learning, it can be difficult to help preservice teachers transition from university classrooms to field placements in ways that provide them with opportunities to enact ambitious instructional strategies. This need is particularly acute in mathematics and science education, where classrooms that model strong disciplinary discourse and argumentation are not always prevalent. This challenge is amplified by the COVID-19 pandemic environment; with schools and universities across the nation operating online, many preservice teachers will miss out on opportunities to practice teaching both within their courses and in K-12 classrooms. To address this urgent challenge in STEM education, project researchers will develop, pilot, and refine a set of coordinated and complementary activities that teacher education programs can use in both online and face-to-face settings to provide practice-based opportunities for preservice teachers to develop their ability to facilitate argumentation-focused discussions in mathematics and science, a critical teaching practice in these content areas. The practice-based activities include: (1) interactive, online digital games that create targeted practice spaces to engage preservice teachers to respond to students’ content-focused ideas and interactions; (2) facilitating group discussions with upper elementary or middle school student avatars in a simulated classroom using performance-based tasks; and (3) an immersive virtual reality whole-classroom environment that allows for verbal, textual and non-verbal interactions between a teacher avatar and 24 student avatars. The online practice suite, made up of these activities along with supports to help teacher educators use them effectively, represents not just an immediate remedy to the challenge of COVID-19, but a rich and flexible set of resources with the potential to support and improve teacher preparation well beyond the COVID-19 challenge.This study will use design-based research to create this integrated system of practice teaching opportunities. This approach will involve developing and refining the individual practice activities, the integrated online practice suite, and the teacher educator support materials by working with a teacher educator community of practice and engaging up to 20 teacher educators and 400 preservice teachers in multiple rounds of tryouts and piloting during the three-year project. The project will proceed in three phases: a first phase of small-scale testing, a second phase trying the materials with teacher educators affiliated with the project team, and a third phase piloting materials with a broader group of mathematics and science teacher educators. Data sources include surveys of preservice teachers’ background characteristics, perceptions of the practice activities, beliefs about content instruction, perceptions about preparedness to teach, and understanding of argumentation and discussion, videos and/or log files of their performances for each practice teaching activity, and scores on their practice teaching performances. The project team will also observe the in-class instructional activities prior to and after the use of each practice teaching activity, conduct interviews with teacher educators, and collect instructional logs from the teacher educators and instructional artifacts used to support preservice teachers’ learning. Data analysis will include pre and post comparisons to examine evidence of growth in preservice math and science teachers’ beliefs, perceptions, understanding, and teaching performance. The project team will also build a series of analytic memos to describe how each teacher educator used the online practice suite within the mathematics or science methods course and the factors and decisions that went into that each use case. Then, they will describe and understand how the various uses and adaptations may be linked to contextual factors within these diverse settings. Findings will be used to produce empirically and theoretically grounded design principles and heuristics for these types of practice-based activities to support teacher 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": "559", "attributes": { "award_id": "2032321", "title": "Collaborative Research: A comprehensive approach to modeling, learning, analysis and control of epidemic processes over time-varying and multi-layer networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1186, "first_name": "Lawrence", "last_name": "Goldberg", "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": 300000, "principal_investigator": { "id": 1189, "first_name": "Carolyn L", "last_name": "Beck", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1187, "first_name": "Tamer", "last_name": "Basar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1188, "first_name": "Joseph Y", "last_name": "Kim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Numerous natural and engineered systems consist of underlying networked components over which dynamical processes evolve. Examples include the spread of infectious disease processes over human contact and international travel networks, the propagation of peak traffic phenomenon over the transportation infrastructure, the spread of viruses or worms over computer networks, and sharing and re-sharing of posted articles, tweets, or rumors over social media platforms. Engineered systems are increasingly interconnected over various levels of both local and large-scale networks. Developing a stronger understanding at fundamental and analytical levels of how viral processes evolve across different network structures, the rates at which they spread, how the occurrence of multiple viral types and multiple network layers affect the spread process dynamics, and how these processes can be suppressed and/or mitigated by employing deliberate control policies will greatly impact the health, safety and security of a vast variety of systems around the globe. The spread of COVID-19 has clearly had broad implications for the health of people on all six inhabited continents as well as the world's economy. The research proposed herein will substantially enhance our understanding of epidemics such as COVID-19 and lead to general policy guidelines that will help limit the loss of human life and reduce the economic impacts of the virus. The methods to be developed in this project will be beneficial for battling subsequent epidemic outbreaks, a second wave of COVID-19, and on a broader scale general viral process. Throughout this project the PIs will build on their past experiences to make every effort towards recruiting and mentoring students from under-represented groups, and will establish outreach efforts by including undergraduate and local high school student researchers.Although the dynamics of epidemic processes over networks have been extensively studied for the past 10-15 years, past work has been focused largely on SIS and SIR processes spreading over static networks. In the proposed project, our focus will be on modeling, analysis and control of dynamic epidemic processes over large and possibly time-varying networks, comprised of multiple layers at multiple scales. We will specifically consider data-informed modeling and analysis of SAIRS (susceptible-asymptomatic-infected-recovered-susceptible) processes over time-varying networks; this work will include stability and equilibria analysis of the nonlinear dynamics of networked epidemic process models, network structure identification, estimation of parameters and structure from imperfect and non-random data, and development of realizable control strategies from the agent level to societal levels. The research proposed will draw on and contribute to wide-ranging foundational results in mathematical modeling and analysis of infectious diseases, time-varying nonlinear and linear analysis methods, optimization and control-theoretic policy formulation, network inference and analysis, sequential sampling strategies with stochastic sample constraints, and mean-field games over networks.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": "558", "attributes": { "award_id": "2033558", "title": "A1: Knowledge Network Development Infrastructure with Application to COVID-19 Science and Economics", "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": 1182, "first_name": "Lara", "last_name": "Campbell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2021-07-31", "award_amount": 34229, "principal_investigator": { "id": 1185, "first_name": "Michael", "last_name": "Cafarella", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1183, "first_name": "Matthew D", "last_name": "Shapiro", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1184, "first_name": "Oren", "last_name": "Etzioni", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The goal of this project is to build infrastructure for efficient construction of knowledge networks and applications, as well as to demonstrate the system with concrete knowledge networks that describe COVID-19 science and economics. In the short term, this work will lead to high accuracy data resources that will be useful to scientists and policy makers in addressing the virus and its economic impact. Sample goals include enabling a medical researcher to quickly identify relevant candidate drugs, and a policy maker to quickly evaluate the likely impacts of a novel law. The project will create programming tools that will make knowledge networks and their applications far less expensive to build. This infrastructure of programming tools will facilitate the creation of a large and novel set of informational tools and will also significantly expand the set of people who can participate in creating knowledge network resources. Because knowledge networks combine unique data analysis qualities with the topical breadth of the entire World Wide Web, the potential growth of knowledge tools is very large and potentially transformative. This project includes partnerships with a strong set of non-academic and academic partners. This convergence research team will integrate their multidisciplinary expertise in data management, artificial intelligence, programming languages, biomedical topics relevant to COVID-19, and economics, with the other domains represented in the projects funded in the Track A Phase II cohort. Creating this knowledge programming infrastructure and concrete knowledge networks will require solving several technical challenges. The first is an intelligent “knowledge compilation layer” that makes useful but rapidly-changing knowledge networks appear to be stable enough for programmers to use them when writing reliable code. The second is the creation of a mechanism for transparently sharing knowledge resources and debugging information within and across organizations. The third is a method for collecting knowledge provenance metadata — details about how every individual data element was created — via automatic instrumentation of user software. A last challenge is the creation of knowledge-from-document systems that can produce high accuracy knowledge networks with very little explicit human oversight.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": "557", "attributes": { "award_id": "2031841", "title": "RAPID Choices under Short-Term Threats and Behavioral Response to Social Distancing in the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1179, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2023-07-31", "award_amount": 102708, "principal_investigator": { "id": 1181, "first_name": "Ricardo A", "last_name": "Daziano", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1180, "first_name": "So-Yeon", "last_name": "Yoon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Social-interaction restrictions have to be respected to contain the spread of highly contagious diseases such as COVID-19. Ensuring compliance to strict health interventions, however, needs better understanding of individual decisions under risk. Until a vaccine is developed, policy makers not only need to find the best incentives for people to avoid physical proximity, but they also need to create plans for relaxing social distancing in the near future. Behavioral forecasts from the models developed in this project help to guide decisions in both cases. Specifically, this project provides answers to several policy-relevant questions such as: What changes in social behavior are people adopting in response to the disruptions created by the COVID-19 outbreak? Who is more likely to respect health guidelines, including social distancing protocols? What are safety/risk perceptions of physical proximity in public spaces (pharmacies, grocery stores, take-outs at restaurants) and how are these perceptions shaping daily choices? How to create incentives for individuals to sustain social-distancing? How are COVID-19 disruptions causing losses in individual welfare, and how can analysts derive metrics to value such losses? In this project, the research team adopts and significantly adapts crowding research tools and methods in retail and transportation studies to analyze social distancing behaviors as preventive action against threats to health. An innovate virtual-reality-based online survey with choice experiments on social distancing collects time-sensitive behavioral data. The data are modeled using micro-econometric discrete-continuous choice models with structural equations for attitudinal components and heavy-tailed error distributions for preference shocks. Unlike standard thin-tailed distributions, error terms that exhibit heavy tails not only generalize standard assumptions but also address decision-uncertainty behavior. Flexible decision rules under risk are integrated into the discrete-continuous choice models that represent time-use scheduling during total and partial lock-downs. Ultimately, research outcomes from this study provide guidance to policy-makers for how to best implement measures such as social distancing and quarantines in order to control major epidemics, and then how to best phase out these measures.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": "556", "attributes": { "award_id": "2037433", "title": "Build and Broaden: Conference on Social Connections to Promote Individual and Community Resilience in Post-COVID-19 Society", "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": 1173, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 70233, "principal_investigator": { "id": 1178, "first_name": "Zhen", "last_name": "Cong", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 293, "ror": "", "name": "University of Texas at Arlington", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1174, "first_name": "Gautam", "last_name": "Das", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1175, "first_name": "Yuan", "last_name": "Zhou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1176, "first_name": "Aaron", "last_name": "Hagedorn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1177, "first_name": "Ling", "last_name": "Xu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 293, "ror": "", "name": "University of Texas at Arlington", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project consists of a conference, the objective of which is to build partnerships and collaborations among the University of Texas at Arlington (UTA), a Hispanic-Serving Institution (HSI), several other Minority-Serving Institutions (MSIs) in Texas, as well as other leading research institutions across the nation. The conference addresses fundamental research questions of the role of social connections as a key form of social capital for individuals and communities adapting to changes after high-impact disasters and extreme events. Collaborative opportunities among the social sciences, computer sciences, and engineering in innovative data, technology, and methods of studying and strengthening social connections are highlighted. The conference promotes interdisciplinary dialogues as the basis for a series of fruitful collaborations to address critical social needs as society recovers from the COVID-19 pandemic. This hybrid conference includes a two-day live-streamed physical conference, two pre-conference virtual meeting sessions, and a series of post-conference follow-up virtual meetings. Each pre-conference virtual meeting consists of three speakers’ presentations and an open discussion session with an estimate of 25 participants. The physical conference includes three structured speaker sessions and three open discussion sessions, with an estimated 50 participants. Overall, speaker sessions include topics on 1) social connections as key social capital in coping with the COVID -19 crisis, 2) vulnerability and risks of COVID-19 and mitigation impact of social connections among minority and high-risk populations, and 3) innovative methods in investigating the impact of social connections in vulnerability and resilience to COVID-19. Open discussion sessions include topics on 1) the unique roles of MSIs in leading research and dissemination among the most affected communities, and 2) exploring interdisciplinary collaborative opportunities and dialogues. A student-focused poster session is held at the physical meeting, with an estimate of 50 poster presentations and an additional 50 visiting students. The poster session focuses on social connections as a critical component of resilience. Post-conference follow-up meetings and activities concern team building and how the impact of the conference may be sustained.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": 1397, "pages": 1424, "count": 14236 } } }