Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1419&sort=-awardee_organization
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-awardee_organization", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1420&sort=-awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1418&sort=-awardee_organization" }, "data": [ { "type": "Grant", "id": "4538", "attributes": { "award_id": "1525716", "title": "Collaborative Research: Integrated Development of Scalable Mobile Multidisciplinary Modules (SM3) for STEM Education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 15621, "first_name": "Abby", "last_name": "Ilumoka", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2015-09-15", "end_date": "2021-04-30", "award_amount": 481979, "principal_investigator": { "id": 15623, "first_name": "Andreas", "last_name": "Spanias", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 15622, "first_name": "Pavan K", "last_name": "Turaga", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "The central idea in this project is to motivate students to pursue studies in STEM areas by creating and disseminating scalable modules that demonstrate in a compelling manner how math and engineering theory enables modern applications such as those embedded in wireless devices. The goal is to motivate graduates to create high-tech products, enter the high-tech workforce, and become innovators. This project will promote a transformative STEM education agenda by developing and disseminating innovative and scalable content for several courses. These innovative products will promote a positive attitude change towards learning STEM concepts by continuously fusing theory with high tech applications. Objectives include developing a diverse community of users, innovative products with mobile video-streamed content, software training modules for skill building, e-books, and training workshops. The PIs will create and disseminate products on multidisciplinary STEM applications in areas including engineering, arts and media, and earth systems. \n\nThe innovative educational modules will have comprehensive multidisciplinary content to address diverse audiences. Modules will be packaged for mobile delivery and will be multipurpose, multidisciplinary, and will: a) motivate students to learn theory through compelling applications, b) engage students in implementation for skill building and workforce creation purposes, and c) immerse diverse audiences and stakeholders in hands-on workshops for outreach, retention, and recruitment purposes. This project is based on collaboration between ASU and Clarkson University and engages faculty from Johns Hopkins University, Phoenix College, St. Lawrence University, Prairie View A&M University, and Corona del Sol high school. The project will use a mixed-method assessment process (qualitative and quantitative data collection) to build an understanding of the impact of the use of the modules, apps, and other tools on student learning gains, from the individual concept level to more general knowledge about scientific and engineering habits of mind and research practice. Assessments will be done through electronic web tools, pre- and post- quizzes, presentations, one-to-one interviews, and ordinary in-class testing.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "4639", "attributes": { "award_id": "1414374", "title": "US-UK Collab: Risks of Animal and Plant Infectious Diseases through Trade (RAPID Trade)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Ecology of Infectious Diseases" ], "program_reference_codes": [], "program_officials": [ { "id": 16024, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2014-09-01", "end_date": "2020-09-30", "award_amount": 1450000, "principal_investigator": { "id": 16029, "first_name": "Charles", "last_name": "Perrings", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 16025, "first_name": "Richard D", "last_name": "Horan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16026, "first_name": "Peter", "last_name": "Daszak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16027, "first_name": "Gerardo", "last_name": "Chowell-Puente", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16028, "first_name": "Michael R", "last_name": "Springborn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "World trade is a boon to economic development but it also increases the risk of dispersing human, animal, and plant diseases. Disease impacts on crop yields and livestock put global food supplies at risk and newly emergent diseases that move from animals to humans can threaten human health. But because trade is also one of the main drivers of economic development, it is important that it not be disrupted unnecessarily by measures to protect against disease risk. Striking the right balance is currently difficult to achieve, however, because trade impacts are not systematically incorporated into national and international disease risk assessments. This award supports an interdisciplinary and international team who seek to solve that problem by developing new tools for evaluating the disease risks of world trade. The risk assessment tools produced by the project will provide animal, plant, and human health authorities at national and international levels with the capacity to make improved assessment of the disease risks associated with imports, and of the consequences of alternative trade responses. Improving disease risk management will enhance national security and economic well-being by reducing both disease dispersal and the losses caused by trade interdictions. The project also will strengthen collaborations between US and UK scientists and train graduate students and post-doctoral scientists in research.\n\nThe researchers will compile data from multiple secondary sources. Data on plant diseases, livestock and wildlife diseases, disease outbreaks, and global emerging diseases, will be provided by such sources as the National Plant Protection Organizations in the United States and the United Kingdom, the United States Department of Agriculture's Animal and Plant Health Inspection Service, and the UK Food and Environment Research Agency. Public domain databases will provide time series data on trade, including trade volumes, trade values, sanitary and phtyosanitary conditions along trade pathways in exporting and importing countries, as well as Gross Domestic Product (GDP) data to estimate national value at risk. These data will be used to parameterize econometric profit maximization risk models to assess the effects of trader decisions and trade networks on the transmission of disease. The models will consider the intervention in trade at three spatial scales (local, national, and global) on incidence of disease transport and effects of altered trade on economic development. The models will be incorporated into a virtual laboratory decision support system to help evaluate alternative incentive-based trade management practices and the effects of decisions on the risk of disease spread.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "5187", "attributes": { "award_id": "0838159", "title": "The Effect of Trace Amounts of H2O on the Rates and Mechanisms of Olivine and Enstatite Phase Transformations in Earth's Mantle Transition Zone", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Petrology and Geochemistry" ], "program_reference_codes": [], "program_officials": [ { "id": 18391, "first_name": "Sonia", "last_name": "Esperanca", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2009-07-01", "end_date": "2013-09-30", "award_amount": 293851, "principal_investigator": { "id": 18392, "first_name": "Thomas", "last_name": "Sharp", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "\"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).\" \n\nThrough plate tectonics and subduction, Earth recycles oceanic crust and lithosphere into its mantle. The down going oceanic crust carries H2O, which, when released into the overlying mantle wedge, causes melting that results in arc volcanism. If the mantle component of the subducting oceanic plate is also hydrated, then some of the subducted H2O is likely to be carried deep into Earth's mantle. High-pressure experiments have shown that minerals from the upper mantle and especially the transition zone (410 to 660 km depth) are capable of storing many oceans worth of H2O. However, we do not yet know how much H2O is actually stored in Earth's mantle or recycled through by subduction. Deep and dangerous earthquakes associated with subduction (deep-focus earthquakes) occur from 400 km up to 700 km where conventional earthquake mechanisms are not possible. The mostly widely accepted hypothesis for the origin of deep-focus earthquakes is that they are triggered by the mineral olivine, which is subducted into the transition zone and beyond where it is stable. This hypothesis requires very slow reaction rates in the relatively cool interiors of subducting plates. The purpose of this research is to determine how trace amounts of H2O enhance olivine and enstatite transformation rates at high-pressure. These data will provide a test of the transformational faulting model and constrain the maximum H2O content compatible with deep focus earthquakes by transformational faulting. This research will provide a better understanding of deep earthquakes and the potential deep-Earth hydrologic cycle. \n\nPrevious studies on the olivine-wadsleyite and the olivine-ringwoodite phase transformations have shown that H2O greatly increases transformation rates. Preliminary experimental studies have also shown that 290 ppm D2O in olivine increases ringwoodite growth rates by two orders of magnitude at 1100°C and reduces the activation enthalpy from around 392 kJ/mol for anhydrous olivine to 274 kJ/mol. Based on their kinetic data and thermo-kinetic models of subduction zones, 290 ppm H2O is enough to eliminate the survival of metastable olivine as a mechanism for deep focus earthquakes. Additional results for 30 ppm H2O in olivine indicate similarly fast growth rates and low activation enthalpy. It is proposed to continue these investigation of the olivine-ringwoodite transformation with 30 ppm H2O, using San Carlos olivine hydrated in the piston-cylinder apparatus. These samples will be partially transformed at 18, 19, 20ad 21 GPa and 700, 900 and 1100 °C, using a multi-anvil apparatus, to determine the pressure dependence of ringwoodite growth for 30 ppm H2O. Transformation rates and mechanisms will also be investigated for enstatite, the second most abundant mineral in the mantle, hydrated under the same conditions as our olivine. The team will also use scanning and transmission electron microcopy to characterize transformation mechanisms in their samples. The results will be used in thermo-kinetic models of subduction zones to determine the maximum H2O contents of olivine and enstatite that are compatible with deep focus earthquakes by transformational faulting.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9591", "attributes": { "award_id": "2217953", "title": "A Bioarchaeological Investigation of Mobility and Infectious Disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Biological Anthropology" ], "program_reference_codes": [], "program_officials": [ { "id": 3025, "first_name": "Rebecca", "last_name": "Ferrell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-15", "end_date": "2025-07-31", "award_amount": 415000, "principal_investigator": { "id": 9422, "first_name": "Jane", "last_name": "Buikstra", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25348, "first_name": "Anne C", "last_name": "Stone", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25349, "first_name": "Kelly J", "last_name": "Knudson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25350, "first_name": "Allisen C", "last_name": "Dahlstedt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25351, "first_name": "Kelly E", "last_name": "Blevins", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Tuberculosis is second only to COVID-19 as a worldwide cause of death by a single pathogen. Its history reflects extraordinary resiliency, which involves a remarkable number of nonhuman hosts in its global spread. This project, which uses molecular and biogeochemical methods, extends knowledge of the history and spread of tuberculosis in relationship to human mobility across ancient communities from a diverse landscape. Studying humankind’s past experience with epidemic disease over time encourages attention to the factors responsible for disease spread and importance of viewing disease in the broadest possible landscape—one that includes the environment and all microbes and potential hosts. This study reaches the public through active websites, presentations, publicly available YouTube videos in both English and Spanish, as well as in-person presentations. The project fosters international research collaborations and provides research experiences for undergraduate and graduate students and professionals in molecular techniques, biogeochemical analyses, and data analysis that will help them be competitive in the job market. \n\nThis study explores the spread of a form of tuberculosis that severely affected communities throughout the Americas long before European contact. In so doing, it considers intersecting identities and mobility patterns, along with human interactions with other species (pinnipeds, bacteria) in contrastive environmental settings. The database reflects a comprehensive survey of a region with extensive evidence of ancient tuberculosis. By combining molecular and skeletal evidence with biogeochemical indicators of paleomobility, this research can characterize the complex cycles of tuberculosis introduction and spread from a pinniped source along the coast to inland communities where it apparently became a human disease. The study considers the complex routes by which tuberculosis can spread across communities. Hypotheses specifically address expected outcomes of ongoing introductions from sea mammals and humans as novel primary hosts and disease spreaders. An important part of this project is to characterize the mobility and interaction patterns of individuals and communities that present evidence of Mycobacterium tuberculosis complex diseases. Such evidence is crucial to developing nuanced models for disease spread across time and space. The data generated and results are to be published in peer-reviewed articles and formats accessible to the public.\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": "10113", "attributes": { "award_id": "2048223", "title": "CAREER: Learning and Leveraging the Structure of Large Graphs: Novel Theory and Algorithms", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Comm & Information Foundations" ], "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": "2021-09-01", "end_date": "2026-08-31", "award_amount": 595527, "principal_investigator": { "id": 5808, "first_name": "Gautam", "last_name": "Dasarathy", "orcid": "https://orcid.org/0000-0003-2252-2988", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "From genetic interaction networks and the brain to wireless sensor networks and the power grid, there exist many large, complex interacting systems. Graph theory provides an elegant and powerful mathematical formalism for quantifying and leveraging such interactions. Unsurprisingly, many modern tasks in science and engineering rely on the discovery and exploitation of the structure of graphs. Unfortunately, there is a stark disconnect between the purported capabilities of data-driven algorithms for graph analytics and their real world applicability. Specifically, the following key challenges emerge for existing algorithms: (i) Reliance on large number of expensive experiments/measurements; this is prohibitive in the large systems typically encountered in science and engineering. (ii) Reliance on the availability of curated and labeled datasets; this is untenable outside a narrow set of disciplines. (iii) Design for worst-case scenarios; this lack of adaptivity to structure unique to the problem severely impairs their statistical and computational efficiency. In response to the above challenges, this research program will close the loop on traditional machine learning systems where data acquisition and learning algorithms are designed separately. The project will devise several novel compressive, adaptive, and interactive algorithms that efficiently exploit structure in the problem. These will be complemented by foundational advances to the theory of learning and leveraging structure in graphs. The methodological advances will have impact on diverse areas such as resilient cyber-infrastructure, robust neuroimaging, and intervention design for pandemics. The research activities are tightly integrated with a comprehensive education, mentoring, and outreach plan that will increase awareness, access, and inclusion in STEM, especially with respect to data-driven methods in science and engineering. \n\n\nThe technical contributions of this project are organized into two interrelated themes: (1) Learning the structure of graphs from compressively and interactively acquired data. The research in this theme will reveal new and interesting tradeoffs between the cost of data acquisition and statistical accuracy. These will be complemented by minimax optimal algorithms that achieve various points in the tradespace. (2) Leveraging the graph structure to accomplish efficient inference. The research in this theme is unified by the general problem of level set estimation on graphs and will result in foundational contributions to the theory of nonparametric learning, meta-learning, and sequential decision making. The research themes feature extensive experimental validation, collaboration with domain experts, and translational activities with the view of driving meaningful and long-term impacting on practice.\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": "10437", "attributes": { "award_id": "2204082", "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)", "FD-Fluid Dynamics" ], "program_reference_codes": [], "program_officials": [ { "id": 573, "first_name": "Ron", "last_name": "Joslin", "orcid": null, "emails": "", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2025-03-31", "award_amount": 214373, "principal_investigator": { "id": 26435, "first_name": "Juan M", "last_name": "Lopez", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "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 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 lung surfactant. The numerical models will then be used to simulate physiologically relevant scales, not accessible experimentally. In addition to understanding 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.\n\nThe 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. 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.\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": "10451", "attributes": { "award_id": "2200161", "title": "PIPP Phase I: Computational Foundations for Bio-social Modeling of Unseen Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 1123, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2024-02-29", "award_amount": 897531, "principal_investigator": { "id": 26449, "first_name": "Pavan", "last_name": "Turaga", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26445, "first_name": "Giulia", "last_name": "Pedrielli", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26446, "first_name": "Gautam", "last_name": "Dasarathy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26447, "first_name": "Visar", "last_name": "Berisha", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26448, "first_name": "Patricia A", "last_name": "Solis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Pandemics unfold in a social, behavioral, and decision-making context that alters the geospatial patterns of spread, depending on an existing underlying landscape of risk and adaptive behavior. Layering socioeconomic factors into traditional predictive modeling frameworks is not sufficient to understand this complexity, nor does it account for the dynamics and vicissitudes of human behavior and free-will. Unexpected human behaviors play a major role, as well as broader factors such as vaccine availability, seasonal effects from human contact patterns, viral environmental persistence, and federal and state-level policy changes around masking and business closures. Enumerating a finite list of factors that should form the basis of a predictive model itself seems like a grand challenge. This project will advance modeling as a continuous, iterative, and dynamic component of pandemic response, where incremental predictions are far more robust, and approaches that innately allow for complexity, adaptation, and surprise can be expected to be operationally useful.\n\nPandemic prevention for unseen pandemics requires several interconnected efforts across immunology, mechanistic modeling, data-driven modeling, and understanding sociopolitical contexts of decision making. Technical aspects of the project include machine learning based tools for predicting immune response from pathogen mutations, switching dynamical systems based models of time-series for fast adaptation, adaptive population sampling techniques, and model predictive control methods for designing behavioral interventions. The project will develop integrative protocols and frameworks that a) leverage techniques for using binding patterns of pathogens for never-before-seen viruses and advances in wastewater-based epidemiology, b) understand the variation in performance of predictive models over geospatial scales using regularizing models, c) design effective interventions under resource constraints, and d) understand their impact on policy making. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG).\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": "10459", "attributes": { "award_id": "2129185", "title": "Student Travel Support to 3D Printing of Polymeric Composites & Hybrid Systems Symposium at American Chemical Society National Meeting; San Diego, California; March 20-24, 2022", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "AM-Advanced Manufacturing" ], "program_reference_codes": [], "program_officials": [ { "id": 2084, "first_name": "Khershed", "last_name": "Cooper", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-06-01", "end_date": "2022-05-31", "award_amount": 47984, "principal_investigator": { "id": 26464, "first_name": "Kenan", "last_name": "Song", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award provides travel support for students, post-docs and young faculty to attend and participate in the Symposium on \"3D Printing of Polymeric Composites and Hybrid Systems \" at the Spring American Chemical Society (ACS) national meeting in San Diego, CA, in March 20-24, 2022. The symposium focuses on new research in the field of polymer processing, composite and hybrid systems and additive manufacturing. The students present through oral and poster sessions their latest research results to the polymer processing, advanced manufacturing and broader engineering communities. Priority is given to student and young faculty participants who are women or who come from underrepresented minority groups. This approach promotes diverse participation at the conference, in the short term, and in STEM fields, in the long term. This award benefits the nation through the education of a skilled and diverse manufacturing workforce, which is better prepared to provide transformative solutions the challenges in their chosen fields.\n\nThis participant support is expected to benefit the students' professional, scientific and technical development. Attendance at the conference gives the students and young faculty a broader view of the polymer processing, composite and hybrid systems, additive manufacturing and engineering profession and of state-of-the-art research in their fields via access to several technical and professional development talks by leading domestic and international speakers. In particular, the symposium will discuss sustainability, recycling and remanufacturing. It will also discuss 3D printing strategies to mitigate polymer waste. Students enhance their communication skills through oral and poster presentations, in-depth discussions of their work with peers in their fields. This interactive experience significantly broadens student education, increases their enthusiasm for their research topic, acquaints them with expectations for scientific careers, and exposes them to new approaches for innovative research.\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": "11025", "attributes": { "award_id": "2302969", "title": "Collaborative Research: NSF-CSIRO: HCC: Small: Understanding Bias in AI Models for the Prediction of Infectious Disease Spread", "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": 946, "first_name": "Hector", "last_name": "Munoz-Avila", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-04-01", "end_date": "2026-03-31", "award_amount": 97154, "principal_investigator": { "id": 26993, "first_name": "Matthew", "last_name": "Scotch", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Artificial intelligence (AI) provides powerful techniques for understanding and prediction of complex systems such as modeling and predicting the spread of infectious diseases. Despite this, these predictive capabilities are rarely adopted by public health decision-makers to support policy interventions. One of the issues preventing their adoption is that AI methods are known to amplify the bias in the data they are trained on. This is especially problematic in infectious disease models which leverage available large and inherently biased spatiotemporal data. These biases may propagate through the modeling pipeline to decision-making, resulting in inequitable and ineffective policy interventions. This project investigates how the AI disease modeling pipeline can lead from biased data to biased predictions and to derive solutions that mitigate this bias in three aims: 1) creating an AI system to predict the spread of emerging infectious diseases in space and time, 2) simulating a population from which we will collect data often used as input for AI systems in a way that the bias is controlled, and 3) exploring links between bias in the collected data and the resulting bias in the AI model and deriving solutions for their mitigation. The project will enable AI-driven infectious disease models and predictions that will support fair and equitable decision-making and interventions. The project will enrich education and training related to ethical AI practices and will support professional development opportunities for early-career researchers, graduate, undergraduate, and high school students in the United States and Australia. \n\nIn Aim 1, the team of researchers will use a self-supervised contrastive learning approach that uses mobility prediction as a pre-text task to learn representations of spatial regions. These representations can be used for infectious disease spread prediction given only very little infectious disease ground truth data. The investigators hypothesize that such a model is susceptible to data bias. Thus, in Aim 2, the team of researchers will leverage a large-scale agent-based simulation that will serve as a sandbox world for which we have perfect knowledge of and from which we can collect data and inject various types of bias. For Aim 3, the team of researchers will investigate how different types of simulated data bias leads to biased AI predictions by leveraging different metrics of fairness in AI and studying how these fairness measures can be incorporated into the AI optimization procedure to mitigate bias. By understanding, measuring, and mitigating bias inherent to traditional AI solutions, the project will enable accurate, scalable, and rapid predictions to support fair and equitable decision-making for pandemic prevention.\n\nThis is a joint project between researchers in the United States and Australia funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organization (CSIRO).\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": "11257", "attributes": { "award_id": "2329358", "title": "Collaborative Research: EAGER: International Type II: Assessing the Role of Social Innovation for Resilience in Global Collaborative Research", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director", "International Research Collab" ], "program_reference_codes": [], "program_officials": [ { "id": 1546, "first_name": "Maija", "last_name": "Kukla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2024-06-30", "award_amount": 167690, "principal_investigator": { "id": 27267, "first_name": "Julia", "last_name": "Melkers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Part 1.\nA paramount scientific challenge is to learn from the COVID-19 pandemic crisis given the uncertainty of the timing and permanent effects on international research. Even in the unlikely event of pandemic eradication, new work patterns may replace pre-pandemic norms and practices of international collaboration. While technological solutions matter for facilitating adaptation to the pandemic, social innovations at the individual and team levels are equally or more important for generating resilience within scientific teams during the protracted crisis. The future of international collaborative work can benefit from an understanding of how scientists have shown team resilience during this time, strengthening teams, and meaningfully transferring knowledge. In an increasingly globalized scientific landscape the grand policy question is how to internationalize scientific teams while, at the same time, responding to local demands. Our goal is to characterize different forms of social innovation in international collaborative work during the pandemic, while taking into account local contexts, opportunities, and institutional factors.\n\nPart 2.\nThis project addresses the topic of global social innovation in science capacity in the face of the COVID-19 pandemic by examining three intertwining features of the social dynamics of international collaborative teams: Social innovation, Adaptation and Resilience, and Learning and Transferability. Social innovation refers to new and different ways of modifying individual and group behavior within the context of team science. The research design builds on interdisciplinary knowledge about individual conduct and group dynamics within the context of teams. The project involves a series of case studies focused around distinct internationally collaborative teams across four countries: Austria, Latvia, Spain and the United States. Data are drawn from bibliometric data sources, semi-structured interviews, focus groups, and a survey of researchers. Teams include new emergent collaborations that establish norms for interaction during the pandemic, or adaptive collaborations that adjust to the barriers and constraints of the pandemic. A novel methodological approach to identifying teams for case study is employed by implementing advanced computing techniques in a new and robust bibliometric dataset, complemented by other snowball sampling techniques. The project will conclude with an international workshop to share and disseminate findings that further international collaboration.\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 } } ], "meta": { "pagination": { "page": 1419, "pages": 1424, "count": 14236 } } }