Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&sort=-id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-id", "next": null, "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1404&sort=-id" }, "data": [ { "type": "Grant", "id": "333", "attributes": { "award_id": "2151970", "title": "Forced Displacement and Community Resilience: Housing Insecurity under COVID-19 in Inland Southern California", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 587, "first_name": "Daan", "last_name": "Liang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2025-06-30", "award_amount": 336050, "principal_investigator": { "id": 589, "first_name": "Qingfang", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 588, "first_name": "Wei", "last_name": "Kang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The unique nature of the COVID-19 pandemic created a disaster situation that highlights the importance of stable housing, particularly as recent evidence suggests that eviction increased the risk of COVID-19 infection and mortality. This study will improve knowledge of processes and consequences of evictions before and after the COVID-19 pandemic in Inland Southern California. The first goal is to analyze the demographic and socioeconomic profile of renters who recently experienced an eviction, as well as the relocation process and outcome. The second goal is to analyze how community resilience and neighborhood change, such as neighborhoods that are gentrifying or becoming more impoverished, are tied to outcomes for renters. The third goal is to evaluate whether and how these outcomes are changed by the pandemic. This study will advance our understanding of involuntary residential choices under an external shock like a pandemic. It contributes to resilience scholarship and helps us understand why the root cause of socioeconomic disadvantage is the primary source of vulnerability under disastrous events, and how housing security interacts with community resilience. As eviction is linked to social, economic, and health disparities, and urban poverty, effective eviction-prevention initiatives could go a long way toward addressing these enduring problems. This study provides evidence for policy interventions designed to address eviction and stem its consequences. It will also provide significant implications for practice and policy in housing markets and social welfare to alleviate social and spatial divides by race, ethnicity, and class that have been exacerbated by the pandemic disruption. This study investigates formal and informal eviction and neighborhood change before and after the COVID-19 pandemic in Inland Southern California using a multiscalar, comparative, and mixed-methods framework. Using both public and restrictive datasets, this study will model the prevalence of eviction and the threat of it at both the household and neighborhood levels. As residential mobility shapes the future life course of evicted households and neighborhood dynamics, the team will model the residential choice of evicted renters and neighborhood dynamics. Further, the project conducts in-depth interviews with tenants, landlords, real estate agents and housing developers, non-profit organizations, and government officials to examine the pathways through which individual characteristics, neighborhood environment, and institutional forces contribute to eviction. The multiscalar, mixed-methods and comparative framework will advance knowledge on the process of eviction at the household level, as well as neighborhood dynamics, policy interventions, power relations, and the coping process of local communities during a pandemic-like disruption. Findings from this study will not only directly benefit policymaking and practice in this region, but also contribute to knowledge in the field for national audiences.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": "332", "attributes": { "award_id": "2214168", "title": "RAPID: The Impact of COVID on Childrens Well-being in 2022: Continued Evidence from the Understanding America Study", "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": 584, "first_name": "Robert", "last_name": "Ochsendorf", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2023-02-28", "award_amount": 200000, "principal_investigator": { "id": 586, "first_name": "Anna R", "last_name": "Saavedra", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Inquiry-based instruction']", "approved": true, "websites": "['https://cesr.usc.edu/people/staff/asaavedr', 'https://uasdata.usc.edu/index.php']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 152, "ror": "https://ror.org/03taz7m60", "name": "University of Southern California", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 585, "first_name": "Morgan", "last_name": "Polikoff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 152, "ror": "https://ror.org/03taz7m60", "name": "University of Southern California", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has been a tremendous disruption to the education of U.S. students and their families, and evidence suggests that this disruption has been unequally felt across households by income and race/ethnicity. While other ongoing data collection efforts focus on understanding this disruption from the perspective of students or educators, less is known about the impact of COVID-19 on children’s prek-12 educational experiences as reported by their parents, especially in STEM subjects. This study will build upon the team's prior research from early in the pandemic. Researchers will continue to collect data from families and aims to understand parents’ perspectives on the educational impacts of COVID-19 by leveraging a nationally representative, longitudinal study, the Understanding America Study (UAS). The study will track educational experiences during the Spring and Summer of 2022 and into the 2022-23 school year. The team will analyze student and family overall and for key demographic groups of interest as schooling during the pandemic continues. This RAPID project allows critically important data to continue to be collected and contribute to continued understanding of the impacts of and responses to the pandemic by American families.Since March of 2020, the UAS has been tracking the educational impacts of COVID-19 for a nationally representative sample of approximately 1,400 households with preK-12 children. Early results focused on quantifying the digital divide and documenting the receipt of important educational services--like free meals and special education services--after COVID-19 began. This project will support the continued targeted administration of UAS questions to parents about students’ learning experiences and engagement, overall and in STEM subjects. The team will conduct data analysis and disseminate findings and results to key stakeholder groups. Findings will be reported overall and across key demographic groups including ethnicity, disability, urbanicity, and socioeconomic status. This project will also produce targeted research briefs addressing pressing policy questions aimed at supporting intervention strategies in states, districts, and schools moving forward. All cross-sectional and longitudinal UAS data files will be publicly available shortly after conclusion of administration so that other researchers can explore the correlates of, and outcomes associated with, COVID-19.This RAPID award is made by the DRK-12 program in the Division of Research on Learning. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.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": "331", "attributes": { "award_id": "2146828", "title": "CIF: Small: Group Testing for Epidemics Control", "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": 581, "first_name": "Scott", "last_name": "Acton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2025-02-28", "award_amount": 500000, "principal_investigator": { "id": 583, "first_name": "Christina", "last_name": "Fragouli", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 582, "first_name": "Paulo", "last_name": "Tabuada", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project develops testing and intervention (quarantine) methods in the presence of a pandemic. COVID-19 has revealed the key role of epidemiological models and testing in the fight against disease spreading. For any new virus or variant of the existing ones, society will always need to be able to expeditiously deploy strategies that allow efficient testing of populations and empower targeted interventions. Group testing is a method that has recently attracted attention for efficient testing, as it allows to identify the infected individuals in a population with many fewer tests than the ones needed to test everyone individually. A main new observation in this project is that viral diseases like SARS-CoV-2 are governed by community spread, and taking into account (even partial) knowledge of the community structure in an epidemics model (e.g., the distribution of students in classes of a school) can make such testing much more efficient and effective.Preliminary results indicate that it is possible to estimate the infection spread and evaluate the impact of interventions using a much smaller number of tests than traditional techniques.Accordingly, the goal of the project is to leverage community structure and epidemic dynamics to enable real-time estimation of infection and intervention with the following attributes: (i) it is robust to model uncertainties; (ii) it offers provable theoretical performance guarantees and (iii) it achieves low complexity of operation. To do so, the proposal combines tools from coding theory and control, and proceeds in two steps. First, assuming complete and perfect knowledge of the true underlying dynamical model, it derives test designs and intervention strategies, as well as fundamental bounds on the number of tests and amount of intervention, for both a static and state-estimation problem formulation. Building on this first step, the proposal then considers approximations to the dynamic models either because the exact dynamics are not perfectly known, or for complexity-reduction reasons. In particular, the proposal develops approximations on the evolution of marginal probabilities for popular epidemic models, derives and analyzes discretized models, explores the effect of parameter uncertainty and investigate decomposable community models; in all cases, the goal is to understand how these approximations provably affect the associated fundamental bounds, test designs, and intervention strategies to contain the disease.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": "330", "attributes": { "award_id": "2151990", "title": "RUI: Mathematical Modeling of Immune Response to SARS-CoV-2", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 579, "first_name": "Amina", "last_name": "Eladdadi", "orcid": null, "emails": "", "private_emails": null, "keywords": "[]", "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2025-08-31", "award_amount": 102482, "principal_investigator": { "id": 580, "first_name": "Hwayeon", "last_name": "Ryu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 150, "ror": "https://ror.org/01szgyb91", "name": "Elon University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 150, "ror": "https://ror.org/01szgyb91", "name": "Elon University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This research investigates the human immune response to SARS-CoV-2 virus to elucidate the key mechanisms responsible for disease severity exhibited by some COVID-19 patients. Despite a significant volume of clinical and experimental studies for the detailed mechanisms of SARS-CoV-2 virus, there is a lack of understanding about the host immune response to the virus, which is largely responsible for the variability in disease severity. To accelerate and supplement our understanding of key target pathways in the immune response, this project will develop and analyze a fundamental, comprehensive model for the host immune dynamics of SARS-CoV-2. Given we continue as a nation under pandemic conditions with new variants emerging and vaccination rollouts, the theoretical explorations through mathematical modeling will serve as a complement to lab-based and data-based approaches. Other features of this work include student involvement in this research, development of a network of collaborators across three institutions, curricula development, recruitment of students from underrepresented groups, and efforts to bring a broad community of researchers studying the host immune dynamics of COVID-19 together to advance our understanding of the interactions between the immune system and SARS-CoV-2. This project aims to accomplish two specific research goals: (i) development of a mathematical model of the host immune dynamics of COVID-19; and (ii) exploration of the model to address important COVID-19 treatment-related questions. For the first goal, the PI will develop and analyze a mathematical model that explicitly represents the virus, immune cells, cytokines, and their interactions, formulated in a system of coupled ordinary and delay differential equations. The main objective is to obtain a better understanding of key aspects of immune response to SARS-CoV-2, specifically its sensitive pathways. For the second goal, the PI will investigate the importance of timing of specific immune responses in disease severity and divergent outcomes, and the emergence of the so-called cytokine storm, excessive production of proinflammatory cytokines in the immune system. The aim is to identify the key mechanisms responsible for disease severity, which could help to identify other pathways to target therapeutically. The primary tools to be used for this project are model parameterization using a series of clinical and experimental data, sensitivity analysis, and numerical simulations. The primary mathematical contribution is the development of computational techniques to analyze high-dimensional nonlinear dynamical systems. In addition, the results from this study on the mechanisms involved in COVID-19 pathology and identification of several therapeutic targets would provide hypotheses to be clinically tested, thus, serving as a foundation for the development of evidence-based treatment protocols to address the global challenge.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": "329", "attributes": { "award_id": "2149450", "title": "NSF-BSF: Willingness to Vaccinate Against COVID-19: Psychological Mechanisms and Ways to Increase Responsiveness", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 577, "first_name": "Claudia", "last_name": "Gonzalez-Vallejo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2025-02-28", "award_amount": 83037, "principal_investigator": { "id": 578, "first_name": "Paul", "last_name": "Slovic", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 149, "ror": "", "name": "Decision Science Research Institute", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 149, "ror": "", "name": "Decision Science Research Institute", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has claimed the lives of over 4 million people worldwide, and over 190 million people have been affected in varying degrees of severity. The vaccine against the coronavirus has dramatically reduced the number of infected people, saving the lives of millions. However, although the vaccine has been proved to be highly effective, and its safety profile is satisfactory, in most countries there is still a relatively large percentage of people who are opposed, who endanger the entire population in their country as well as worldwide. Since COVID-19 continues to pose a threat to humans, it is essential to understand what causes this resistance and to find ways to increase vaccination rates. This research relies on previous works on terror management theory, as well as on insights from recent work on people's willingness to donate organs after death, to suggest psychological mechanisms that may explain people’s resistance to the vaccine, and to offer effective interventions to increase vaccination rates.Given the necessity of getting more people vaccinated, understanding the reasons behind peoples’ negative attitudes towards Covid vaccines and their reluctance to be vaccinated is of great importance. This research analyzes the multiple factors that may influence people’s attitudes toward the coronavirus and their decisions to be vaccinated against COVID-19 or not. The study entails 11 experiments reflecting two research directions: The first (Part I) examines the influence of different descriptions of COVID-19 on people’s attitudes and willingness to be vaccinated. Manipulations to increase thoughts of life saving (rather than death) may override defense mechanisms that might create negative attitudes toward the vaccine, thus increasing willingness to vaccinate. Additional studies manipulate the status quo (making the decision not to vaccinate a deviation from the default) and examine the effect on risk perceptions and on increasing willingness to vaccinate. Part II of the studies focus on individual differences in fears and beliefs that are hypothesized to play a major role in people’s decisions about vaccination, including the fear of death, religious beliefs, belief in a just world and belief in tempting fate.This project is being supported by a partnership between the National Science Foundation and the U.S.-Israel Bi-national Science Foundation.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": "328", "attributes": { "award_id": "2204081", "title": "Collaborative Research: Transport of model-virus through the lung liquid lining", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 575, "first_name": "Ron", "last_name": "Joslin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2025-03-31", "award_amount": 285503, "principal_investigator": { "id": 576, "first_name": "Amir H", "last_name": "Hirsa", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 148, "ror": "https://ror.org/01rtyzb94", "name": "Rensselaer Polytechnic Institute", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 148, "ror": "https://ror.org/01rtyzb94", "name": "Rensselaer Polytechnic Institute", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The novel coronavirus SARS-CoV-2, responsible for the COVID-19 pandemic, is similar to other respiratory coronaviruses, such as SARS-CoV (2002) and MERS-CoV (2012). All these viruses cause dangerous respiratory disorders with high mortality and grave impacts on society. This virus destroys the cells that produce lung surfactants which, among other things, keep the alveoli air-sacks from collapsing, and eventually the lungs fill with liquid. The two primary functions of lung surfactants are regulating the interfacial tension and surface viscosities of the liquid lining of the alveoli, and providing a first line of immune defense against airborne pathogens. The fluid dynamic interactions between the liquid lining of the lung, the lung surfactants, and the respiratory virus are presently not well understood. This project addresses this gap by conducting experiments and numerical modeling to capture the essential fluid dynamics of a model virus interacting with the primary insoluble component of lung surfactant. The expansion and contraction of the alveoli will be modeled using an open cavity with oscillatory sidewalls that expand and compress the liquid layer. The numerical models will then be used to simulate physiologically relevant scales, not accessible experimentally. Aside from the flow in the lung liquid lining, the present knowledge gap in predictive modeling of interfacial dilation and compression is hampering developments in other areas, such as in water waves, which are of utmost importance in modeling of carbon dioxide gas exchange between the atmosphere and the oceans.Predictive models for the transport of small particles in a surfactant-covered liquid layer will be developed. A key advancement in the proposed modeling of surface elasticity is to measure the equation-of-state of the monolayer in a state corresponding to that found when it has been subjected to a large number of dilation/compression cycles. The usual approach of determining properties of a recently spread monolayer undergoing slow compression is inappropriate for modeling monolayer hydrodynamics coupled to an oscillatory bulk flow, as the monolayer is in a different state with very different interfacial properties. The role of interfacial dilatational viscosity and its significance relative to surface elasticity remains poorly understood and presents a major impediment to the predictive modeling of free-surface flows. The PIs have a proven track record of productive multidisciplinary collaboration, and will continue to provide a unique educational opportunity for the graduate and undergraduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1405, "pages": 1405, "count": 14046 } } }