Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=5&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=1442&sort=principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=6&sort=principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=4&sort=principal_investigator" }, "data": [ { "type": "Grant", "id": "353", "attributes": { "award_id": "2147222", "title": "DDRIG: Telemedicine and In-person Practices for the Measurement of Pain", "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": 634, "first_name": "Wenda K.", "last_name": "Bauchspies", "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": 13749, "principal_investigator": { "id": 636, "first_name": "Owen", "last_name": "Whooley", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 170, "ror": "https://ror.org/05fs6jp91", "name": "University of New Mexico", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 635, "first_name": "Natalie", "last_name": "Fullenkamp", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 170, "ror": "https://ror.org/05fs6jp91", "name": "University of New Mexico", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true }, "abstract": "This project asks how the shift to telemedicine platforms has affected the quality of care for chronic pain patients within the Veterans Healthcare Administration. This project will inform health care practices by revealing how telemedicine has changed pain assessment, documentation, and treatment. Lessons learned from this study will have relevance for other healthcare organizations that utilize telemedicine for pain assessment. This project considers the medical implications of transforming qualitative, subjective experiences, such as pain, into quantitative data that create clinical accountability. This study illuminates how technological practices like telemedicine are re-defining the creation of medical knowledge, the parameters of expert jurisdiction, and organizational protocols. It will be of interest to patients, health-care providers, policy makers and administrators. This project will analyze patient data pre- and post-COVID through an examination of electronic medical records by comparing in-person and remote care. The project aims to identify how practices—such as measuring pain, documenting pain status, prescribing medication, or recommending alternative treatment modalities—are affected by the migration to telemedicine. In doing so, it assesses how the increased use of telemedicine has challenged common assessment and treatment protocols used by health providers. In tandem, interviews with clinicians will trace when and how health care providers decide to bring patients into the clinic for in-person assessment, rather than relying on telemedicine alone. This study will provide an analysis of the need for creating continuous care plans that bridge distinct delivery platforms. The findings from this project will help policymakers and administrators to support clinicians during both in-person and remote visits for pain care.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": "10438", "attributes": { "award_id": "2203262", "title": "EAGER: A Holistic Heterogeneous Temporal Graph Transformer Framework with Meta-learning to Combat Opioid Epidemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 864, "first_name": "Sylvia", "last_name": "Spengler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-11-01", "end_date": "2023-09-30", "award_amount": 150000, "principal_investigator": { "id": 638, "first_name": "Yanfang", "last_name": "Ye", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The devastating and lethal opioid epidemic has largely been fueled with various opioids in the United States. Unfortunately, driven by considerable profits, opioid trafficking has co-evolved with modern technologies, such as, social media platforms have been utilized for marketing and selling illicit drugs including opioids, which has attracted increasing attention from both public health agencies and law enforcement. As online opioid trafficking activities are nimble and resilient, it calls for novel techniques to effectively detect opioid trades to facilitate proactive response strategies. By advancing capabilities of machine learning and data science, the goal of this project is to design and develop a holistic framework to model and analyze dynamic multi-modal data to fight against online opioid trafficking and, thus, help combat opioid epidemic. This research will enable a conceptual framework for the federal and state governments, public health agencies, law enforcement, and local communities to develop proactive strategies to build up a drug-free world - one community at a time. \n\nBy engaging novel disciplinary perspectives, this exploratory, yet transformative, high risk-high payoff work will involve radically different approaches for the development of an integrated framework to combat online opioid trafficking. The research will have three key components. First, the team will propose a novel heterogeneous temporal graph (HTG) to comprehensively model and abstract multi-modal posts and relational information over time on social media. Second, based on the constructed HTG, the research team will develop an innovative graph transformer to learn user representations for opioid trafficker detection. Third, to tackle the challenge of lack of sufficient labeled data for model training, the team will further develop a new meta-learning algorithm by joining unsupervised graph structure and small amount of supervised training data to update the model. This will enable the model to quickly adapt to a new task, such as identifying a new type of traded opioid and its traffickers on social media, using only a few samples and training iterations. The developed holistic framework for the detection of online opioid trafficking activities will have significant impacts on addressing the critical national opioid epidemic facing our society. The research will be beneficial to data mining and machine learning communities, as well as multidisciplinary domains such as public health, epidemiology, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through novel curriculum development, participation of underrepresented groups, and student mentoring activities.\n\nThis 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": "10397", "attributes": { "award_id": "2203262", "title": "EAGER: A Holistic Heterogeneous Temporal Graph Transformer Framework with Meta-learning to Combat Opioid Epidemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [], "start_date": "2021-11-01", "end_date": "2023-09-30", "award_amount": 150000, "principal_investigator": { "id": 638, "first_name": "Yanfang", "last_name": "Ye", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The devastating and lethal opioid epidemic has largely been fueled with various opioids in the United States. Unfortunately, driven by considerable profits, opioid trafficking has co-evolved with modern technologies, such as, social media platforms have been utilized for marketing and selling illicit drugs including opioids, which has attracted increasing attention from both public health agencies and law enforcement. As online opioid trafficking activities are nimble and resilient, it calls for novel techniques to effectively detect opioid trades to facilitate proactive response strategies. By advancing capabilities of machine learning and data science, the goal of this project is to design and develop a holistic framework to model and analyze dynamic multi-modal data to fight against online opioid trafficking and, thus, help combat opioid epidemic. This research will enable a conceptual framework for the federal and state governments, public health agencies, law enforcement, and local communities to develop proactive strategies to build up a drug-free world - one community at a time. \n\nBy engaging novel disciplinary perspectives, this exploratory, yet transformative, high risk-high payoff work will involve radically different approaches for the development of an integrated framework to combat online opioid trafficking. The research will have three key components. First, the team will propose a novel heterogeneous temporal graph (HTG) to comprehensively model and abstract multi-modal posts and relational information over time on social media. Second, based on the constructed HTG, the research team will develop an innovative graph transformer to learn user representations for opioid trafficker detection. Third, to tackle the challenge of lack of sufficient labeled data for model training, the team will further develop a new meta-learning algorithm by joining unsupervised graph structure and small amount of supervised training data to update the model. This will enable the model to quickly adapt to a new task, such as identifying a new type of traded opioid and its traffickers on social media, using only a few samples and training iterations. The developed holistic framework for the detection of online opioid trafficking activities will have significant impacts on addressing the critical national opioid epidemic facing our society. The research will be beneficial to data mining and machine learning communities, as well as multidisciplinary domains such as public health, epidemiology, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through novel curriculum development, participation of underrepresented groups, and student mentoring activities.\n\nThis 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": "9537", "attributes": { "award_id": "2203261", "title": "CAREER: Securing Cyberspace: Gaining Deep Insights into the Online Underground Ecosystem", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Secure &Trustworthy Cyberspace" ], "program_reference_codes": [], "program_officials": [ { "id": 1740, "first_name": "Sara", "last_name": "Kiesler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-11-01", "end_date": "2024-03-31", "award_amount": 500000, "principal_investigator": { "id": 638, "first_name": "Yanfang", "last_name": "Ye", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "As the Internet becomes increasingly ubiquitous, it offers a low-risk harbor for cybercrime -- illegal activities such as hacking and online scams. Cybercrime is increasingly enabled by an online underground ecosystem, within which are anonymous forums and so-called dark web platforms for cybercriminals to exchange knowledge and trade in illicit products and services. To better protect the trust of legitimate institutions and users within cyberspace, and to disrupt or mitigate the harm of illegal markets, there is an urgent need to gain deep insights into the online underground ecosystem and understand its intricate relationships. The goal of this project is to design and develop an integrated computational framework for in-depth investigation of the online underground ecosystem and thus to help secure cyberspace by producing data-driven interventions of cybercrimes. \n\nThe research will have three main parts. First, the research team will design methods and develop algorithmic and scalable techniques to automate the analysis of online underground markets. Second, the team will develop a novel framework for cross-market user identification and profiling. Third, the team will design an innovative model and propose algorithms for structural analysis of cybercriminal social networks to gain deep insights into the organization and operation of cybercriminals in the ecosystem. The broader impacts of this work include benefits to scientific communities and society as a whole by developing interventions into online crime to secure cyberspace for its users. The establishment of a cybersecurity lab through this project will enhance education and workforce training in cybersecurity. The project integrates research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities for a newly-established cybersecurity degree program.\n\nThis 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": "354", "attributes": { "award_id": "2217239", "title": "III: Medium: A Data-driven and AI-augmented Framework for Collaborative Decision Making to Combat Infectious Disease Outbreaks", "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": 637, "first_name": "Wei", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-11-01", "end_date": "2025-09-30", "award_amount": 540442, "principal_investigator": { "id": 638, "first_name": "Yanfang", "last_name": "Ye", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Infectious disease outbreaks, such as the novel coronavirus disease (COVID-19) pandemic, entailed localized conditions with evolution in time and space present a daunting task for policy and decision makers in finding optimal non-pharmaceutical intervention (NPI) strategies at different scales that balance epidemiological benefits and socioeconomic costs. To help tackle this challenging problem, by harnessing the data revolution and advancing capabilities of artificial intelligence (AI), this multidisciplinary project aims to design and develop a data-driven and AI-augmented framework that is tailored to the evolving localized conditions and enables expert-in-the-loop for adaptive NPIs to effectively respond to the dynamics of epidemic while balancing the multidimensional socioeconomic impacts. The proposed work will not only benefit local and federal governments, regional communities, corporations, societal leaders and the public by assisting with effective responses to the public health issues while mitigating negative socioeconomic impacts and various induced crises, but will also facilitate the development of robust science-based decision support systems responding to future natural or man-made disasters. The research will be beneficial to multidisciplinary areas, including data science, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes (e.g., open-source code, data, and models) will be made publicly accessible and broadly distributed through publications, media presses, etc. This project will integrate research with education, including novel curriculum development, student mentoring, professional training and workforce development, and K-12 outreach activities aimed at underrepresented groups.To combat infectious disease outbreaks with robust response planning, this project includes four interconnected research components to develop an intelligent and interactive decision support framework that allows in silico exploration of extensive possible NPIs prior to the potential field implementation phase. First, the team will develop a novel spatial-temporal heterogeneous graph model to abstract dynamics of harnessed multi-source data. Second, the team will develop new techniques to learn node (i.e., area) representations over the constructed graph by integrating both spatial and temporal dependencies while preserving the heterogeneity. Third, based on the learned node representations, given a set of NPIs, the team will design and develop an innovative NPI-aware multi-head transformer for multi-task prediction (i.e., forecasting epidemic dynamics and associated socioeconomic impacts). Fourth, based on the predictions, the team will develop a novel multi-agent reinforcement learning model with inverse reward learning to enable expert-in-the-loop in finding optimal sequential NPIs that balance epidemiological benefits and socioeconomic costs under certain constraints and objectives set by policy and decision makers. The research will advance the field of information integration and informatics through the development of a series of original works including novel deep graph learning techniques with the context of heterogeneous and dynamic graph structures, which will also provide foundational work for addressing similar challenges for future natural or man-made disasters.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": "355", "attributes": { "award_id": "2222007", "title": "FORTE - FORest ciTizenship for disaster rEsilience: learning from COVID-19", "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": 639, "first_name": "Kwabena", "last_name": "Gyimah-Brempong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2025-04-30", "award_amount": 199552, "principal_investigator": { "id": 641, "first_name": "Peter", "last_name": "Newton", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 172, "ror": "", "name": "University of Colorado at Boulder", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 640, "first_name": "Colleen M Scanlan", "last_name": "Lyons", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 172, "ror": "", "name": "University of Colorado at Boulder", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic had a disproportionately large negative impact on traditional rural societies with little access to modern health care. This research project will study how forest-dependent indigenous people of the Amazon forest engaged in citizenship to exercise their rights to influence institutions and political processes that increased their resilience and reduced their vulnerability to COVID-19. The study will use existing data to quantify the mechanisms through which these communities reduced their vulnerability in the past by being more engaged citizens. The research team, consisting of scientists from Brazil, the UK, and the US, will also collect new data using interviews with people living in traditional rural communities, to better understand the opportunities and barriers to their engagement. The research results will be disseminated to stakeholders, including local, state, and federal governmental agencies, and policy makers in Brazil. The results of this research could provide guidance on policies to strengthen inclusive democratic governance, reduce unequal access to health and other resources, and increase resilience to crises. The results of this research have global application, hence will help establish the US as a global leader in disaster resilience.Citizenship from below, a process through which marginalized peoples self-organize to claim recognition and rights, is crucial to disaster recovery, resilience, and societal renewal. This project assembles an international team of social scientists from Brazil, UK, and the US to study how the forest people of Brazilian Amazonia have used collective action to mitigate the negative impacts of the COVID-19 pandemic and strengthen disaster resilience. The study will: (1) quantify linkages between forest citizenship and COVID-19 resilience; (2) investigate various practices of forest citizenship that were employed during the COVID-19 pandemic; and (3) disseminate learning experiences of the forest people on how to promote forest citizenship and enhance disaster resilience across Amazonia and other parts of the world. The research project will address these questions through three interlinked projects---quantitative analysis of secondary health, governance, and environmental data (entire Brazilian Amazon), qualitative fieldwork in selected municipalities in Amazonia and Acre State, and action-research. The results of this research could provide guidance on policies to strengthen inclusive democratic governance, reduce unequal access to health and other public resources, and increase resilience to crises. The result of this research is scalable, hence can help establish the US as a global leader in disaster resilience.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": "356", "attributes": { "award_id": "2217427", "title": "Exploring the Role of Adaptive Capacity on Democratic Performance (ERAC-DP): Governmental and Nonprofit Organizations in the Pandemic", "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": 642, "first_name": "Kwabena", "last_name": "Gyimah-Brempong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2025-04-30", "award_amount": 199999, "principal_investigator": { "id": 644, "first_name": "Thomas A", "last_name": "Bryer", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 643, "first_name": "Sofia Prysmakova", "last_name": "Rivera", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "This research project uses methods from several social sciences and comparative analysis to study how COVID-19 response policies can increase inequality, foster democratic governance, and political participation of vulnerable people. The research project will answer four questions: (i) what inequalities emerged for vulnerable populations during the COVID-19 pandemic? (ii) how did organizational adaptive capacities affect outcomes for vulnerable populations? (iii) how did adaptive capacities affect policy compliance of vulnerable populations? and (iv) what digital tools will facilitate a sustained collaboration (domestic and international) to enhance global and local action to reduce inequities and increase policy compliance? The study is based on case studies from Atlanta, GA, Montreal, Canada, Manchester, UK, and Warsaw Poland and uses a combination of theories from several social science fields and rich data collection and analyses. The results of this research project will help to answer important questions as to why groups are differentially affected by policies designed to help all and why policy compliance may differ across groups. The results could guide policy formulation and implementation during pandemics and other emergencies.This research uses embedded case studies in four countries---Canada, Poland, UK, and the US---to study how COVID-19 pandemic policies affected inequalities, and how this in turn affected political activism and policy responses of different groups. Specifically, the project will try to answer four questions: (i) what inequalities emerged during the COVID-19 pandemic? (ii) how did adaptive capacities affect outcomes for vulnerable populations? (iii) how did adaptive capacities affect policy compliance by vulnerable populations? and (iv) what digital tools will facilitate sustained collaboration to enhance global and local action to reduce inequities and increase policy compliance? The research will answer these questions using a methodology that combines theories from several social and behavioral sciences. In addition to academic research, the PIs propose to engage diverse policy makers to improve the policy impact of the research project. By addressing the impact of policies on inequality and political participation, this research project will make significant contributions to social and behavioral science research on policy impacts generally. The research results of this research project will provide inputs into policies to reduce inequalities as well as enhance political participation.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": "13725", "attributes": { "award_id": "2127364", "title": "NNA Collaboratory: Collaborative Research: Arctic Cities: Measuring Urban Sustainability in Transition (MUST)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "NNA-Navigating the New Arctic" ], "program_reference_codes": [], "program_officials": [ { "id": 3760, "first_name": "Colleen", "last_name": "Strawhacker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": null, "award_amount": 1798856, "principal_investigator": { "id": 647, "first_name": "Marlene", "last_name": "Laruelle", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 174, "ror": "https://ror.org/00y4zzh67", "name": "George Washington University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 647, "first_name": "Marlene", "last_name": "Laruelle", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 174, "ror": "https://ror.org/00y4zzh67", "name": "George Washington University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, { "id": 15972, "first_name": "Dmitry A", "last_name": "Streletskiy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 15973, "first_name": "Nikolay", "last_name": "Shiklomanov", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 174, "ror": "https://ror.org/00y4zzh67", "name": "George Washington University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, { "id": 29991, "first_name": "Kelsey", "last_name": "Nyland", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 174, "ror": "https://ror.org/00y4zzh67", "name": "George Washington University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "Navigating the New Arctic (NNA) is one of NSF’s 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. This Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by addressing interactions among social systems, natural environment, and built environment in the following NNA focus areas: Arctic Residents, Data and Observation, Education, Global Impact, and Resilient Infrastructure. <br/><br/>Arctic cities face multiple challenges from social and economic transformations, deteriorating infrastructure, a changing environment, and pressures on their governance systems. To respond effectively, mayors, city councils, agency leaders, local citizens and residents, and other stakeholders need a clear set of indicators to help them understand changes in Arctic conditions and provide guidance in devising infrastructure and governance strategies to achieve future prosperity and spur sustainability. This NNA Collaboratory project assesses numerous issues of urban sustainability and compiles a set of metrics on Arctic conditions that provides data about changes in several issues, including the natural environment, energy, and socio-cultural issues. With these indicators, policymakers and stakeholders can develop effective governance systems and design and build infrastructure to meet the challenges of a shifting natural environment and economy in Arctic urban areas. <br/><br/>This NNA Collaboratory expands upon a database of indicators on various topics in numerous cities on natural conditions in the Arctic. The Collaboratory cultivates theories and test hypotheses in the natural, social, and urban planning sciences by developing new indicators for Arctic conditions that include historical data, enabling examination of dynamic trends and relationships among the various components of urban sustainability. The Collaboratory advances a long-term research agenda and platform around Arctic urban sustainability by organizing collaborations among the Arctic research community and facilitating convergence research on the natural, social, and built environment transitions taking place in and around Arctic cities now and in the future. Indicators introduce a shared vocabulary with explicit measures and assumptions that allow scholars and others across disciplines to interact while providing a foundation for theory building and testing. Working with Indigenous and other communities, the Collaboratory also engages in case studies of divergent Arctic urban areas to examine city-level data for interlinkages among assorted elements of sustainability. Consequently, this NNA Collaboratory improves the ability of policymakers and stakeholders to promote sustainability by providing tools to measure progress, identify areas of most urgent need, select verifiable best practices, examine opportunity costs, and determine where external actors can have the greatest impact.<br/><br/>This project is supported by the NNA Program and the Office of International Science and Engineering (OISE).<br/><br/>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": "357", "attributes": { "award_id": "2223914", "title": "Pandemic Communication in Time of Populism: Building Resilient Media and Ensuring Effective Pandemic Communication in Divided Societies", "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": 645, "first_name": "Kwabena", "last_name": "Gyimah-Brempong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2024-04-30", "award_amount": 199516, "principal_investigator": { "id": 647, "first_name": "Marlene", "last_name": "Laruelle", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 174, "ror": "https://ror.org/00y4zzh67", "name": "George Washington University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 646, "first_name": "Daniel C", "last_name": "Hallin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 174, "ror": "https://ror.org/00y4zzh67", "name": "George Washington University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project uses several methods to study how populist politicians distorted COVID-19 pandemic health communication to encourage polarized attitudes and distrust among citizens, thus making them more vulnerable to misinformation generally. It also studies how best to counter these populist narratives and develop more effective communication channels. The research studies four areas of communication: government led pandemic communication, media policy, media coverage, and public attitudes towards the media. The project makes an important contribution to research on populist communication and political polarization by bringing two fields of expertise–populist communication and public health–together. The research will inform recommendations aimed at building more resilient media organizations that are better equipped to withstand the challenges of future pandemics in divided societies. By helping to improve the quality of health communication, this research will help to improve the health, hence living standards of US citizens. This project will develop the first comprehensive, comparative study of health crisis communication in the context of populist politics and examines the impact of populism on four aspects of the pandemic communication circuit during COVID-19: government-led health crisis communication, media policy, media coverage, and public attitudes. The project will also study how best to counter these populist narratives and develop more efficient and reliable communication. The focus is on four countries---Brazil, Poland, Serbia, and the US---all led by populist leaders during the pandemic and capture different types of populist responses to the pandemic. The project also takes a transnational perspective to analyze how the interaction between populism and pandemic communication was shaped by China and Russia’s pandemic geopolitics. By helping to improve the quality of health communication, this research will help to improve the health, hence living standards of US citizens.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": "358", "attributes": { "award_id": "2213390", "title": "LEAPS-MPS: Incorporating Stratification by Vaccination Status and Virus Variants in Mathematical Models of Infectious Disease Spread", "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": 648, "first_name": "Amina", "last_name": "Eladdadi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2024-08-31", "award_amount": 242192, "principal_investigator": { "id": 649, "first_name": "Matthew D", "last_name": "Johnston", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 175, "ror": "https://ror.org/04d52ej85", "name": "Lawrence Technological University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 175, "ror": "https://ror.org/04d52ej85", "name": "Lawrence Technological University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Since the beginning of the COVID-19 pandemic, mathematical modeling has played a significant role in assessing and forecasting the impacts of the disease and guiding public health policy. Existing mathematical frameworks, however, have been slow to adapt to sudden changes in disease spread dynamics resulting from the waning vaccine immunity and emergence of COVID-19 variants such as delta and omicron. This project will address these challenges by developing data-driven mathematical modeling tools which divide populations according to factors that have distinct characteristics, such as those due to differences in vaccination status and the spread of virus variants. As COVID-19 evolves and becomes endemic in the global population, the developed frameworks will guide public health officials in evaluating the effectiveness of potential vaccination strategies and assessing the capacity of variants to alter the course of disease spread. This will facilitate targeted and impactful policies rather than disruptive population-wide restrictions and lockdowns. The project will engage undergraduate students in topical applied mathematics research and support underrepresented students in STEM with a particular focus on the African American community in Metro Detroit. The project will additionally advance curricular and program development at Lawrence Technological University, which will enhance the institution's research environment and further the principal investigator’s professional goal of establishing a sustained, student-focused, and interdisciplinary research program in mathematical biology at Lawrence Technological University.Traditional mathematical modeling frameworks of infectious disease spread often ignore factors of heterogeneous spread within a population. This can lead to poor estimates of epidemiological parameters (such as the basic reproduction number and herd immunity threshold), mistaken assessments of the mechanisms of disease spread, and inaccurate forecasts. This project will develop the theory and application of compartmental SIR-type (Susceptible-Infectious-Recovered) models, which are associated with a system of ordinary differential equations, to incorporate variances in a population's vaccination coverage level, differing waning immunity periods, and competition between virus variants with distinct epidemiological characteristics. Waning immunity will be incorporated through a gamma-distributed delay on return to susceptibility after vaccination or previous infection. The resulting distributed delay differential equations will be analyzed and numerically simulated using the linear chain trick, which reduces gamma-distributed delays to a linear chain of exponential delays. Case data from The Michigan Department of Health and Human Services will be used to parametrize and validate the models with the goal of providing insightful forecasts for the spread of COVID-19 under different immunization schedules. Virus variants will be incorporated by dividing the infectious class into distinct compartments with variant-specific parameters, such as variances in transmissibility, severity, vaccine resistance, reinfection rate, and diagnostic detection. The goal will be to establish novel critical thresholds for when a virus variant can persist or become dominant in a population as well as address the inverse question of estimating a variant's epidemiological parameters from its early-stage growth. By controlling a population's vaccination coverage level, the developed models will be able to cut through the complexity of case incidence data to provide critical insights into the primary factors driving disease spread. User-friendly computational packages capable of implementing the models and interfacing with public health databases will be developed.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": 5, "pages": 1442, "count": 14420 } } }