Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1419&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=1419&sort=-id", "next": null, "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1418&sort=-id" }, "data": [ { "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": 1419, "pages": 1419, "count": 14184 } } }