Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&sort=-approved
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-approved", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-approved", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1406&sort=-approved", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1404&sort=-approved" }, "data": [ { "type": "Grant", "id": "12480", "attributes": { "award_id": "2327791", "title": "Collaborative Research: IHBEM: Multidisciplinary Analysis of Vaccination Games for Equity (MAVEN)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MSPA-INTERDISCIPLINARY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28418, "first_name": "Hyunju", "last_name": "Oh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1862, "ror": "https://ror.org/00376bg92", "name": "University of Guam", "address": "", "city": "", "state": "GU", "zip": "", "country": "United States", "approved": true }, "abstract": "The MAVEN (Multidisciplinary Analysis of Vaccination Games for Equity) project addresses the global health threat of vaccine inequity in the fight against emerging infectious diseases. This project aims to provide a comprehensive understanding of vaccination coverage and identify key drivers of vaccine uptake. This will reduce the risk of future pandemics by enabling targeted interventions to increase vaccine acceptance among vulnerable populations, in particular racial/ethnic/sexual minority and rural populations. The research will also help to address vaccine inequities in the United States that are currently disproportionately affecting minority subgroups and clinical subpopulations (e.g., men with HIV infection). Ultimately, this research will have implications for public health policy and practice, contributing to the global effort to predict and mitigate the impacts of pandemics. In addition to the scientific contributions, the project will provide valuable training opportunities for over ninety undergraduate and graduate students and cultivate a diverse pool of talent equipped to adequately respond to current and future pandemics.The project will answer three questions: Q1) What are the structural, social, and individual factors related to vaccine uptake for common diseases (e.g., influenza), newer pandemics/epidemics, e.g., COVID-19, mpox (monkeypox), and future diseases? Q2) How does population heterogeneity affect vaccination behavior in a community context? Q3) How does individual behavior feedback into mpox (monkeypox) disease dynamics? The project will combine expertise from mathematical epidemiology and social and behavioral sciences to (1) develop a general framework of vaccine uptake that incorporates individual, social, and structural factors by analyzing a variety of secondary datasets, (2) collect primary survey data and use the framework to develop universal vaccine uptake (as well as vaccine refusal) models that are broadly applicable to mpox (monkeypox) and future outbreaks, and (3) use traditional and modern economic models of individual decision-making under uncertainty. Methodologically, the project will develop a new epidemiological-behavioral system of ordinary differential equations, using multiple data sources (observational, survey, and experimental) and mixed methods to estimate people’s vaccination preferences. The project will also integrate the investigators' empirical findings into the new epi-model, and use the parameterized epi-model to conduct retrospective and prospective vaccine acceptance and hesitance/refusal models.This award is jointly funded by the Division of Mathematical Sciences, Division of Social and Economic Sciences in the Directorate of Social, Behavioral and Economic Sciences, and Established Program to Stimulate Competitive Research (EPSCoR).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": "12481", "attributes": { "award_id": "2327816", "title": "Collaborative Research: IHBEM: Three-way coupling of water, behavior, and disease in the dynamics of mosquito-borne disease systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MSPA-INTERDISCIPLINARY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28419, "first_name": "Mary", "last_name": "Hayden", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 490, "ror": "", "name": "University of Colorado at Colorado Springs", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Complex behavioral responses to information from public health officials, social media, and elsewhere during the COVID-19 pandemic laid bare the limitations of the simplistic assumptions that epidemiological models have traditionally made about human behavior. The investigators of this project hypothesize that human behavior may also play a key role in why diseases transmitted by Aedes mosquitoes, such as dengue and Zika, have been so difficult to control. Aedes mosquitoes lay eggs in household water storage containers, meaning that behaviors related to water storage, water consumption, and water container management impact mosquito populations and, thereby, diseases transmitted by these mosquitoes. The central objective of this project is to understand how humans make decisions about preventive actions against Aedes-borne diseases and how those actions in turn affect disease dynamics and subsequent individual-level decision-making. The project will focus on the city of Ibagué, Colombia, where public health officials have long used behavioral approaches to intervene against Aedes-borne diseases. Empirical social science research will investigate how individuals respond to these interventions and characterize differences among individuals in their responses. Mathematical modeling research will estimate the effectiveness of these interventions at the population level. Throughout the project, a close connection with community members and local public health officials will be cultivated to ensure the effective translation of project outcomes. Training and capacity building activities will extend the impacts of the project to settings beyond Ibagué.This project aims to develop a mechanistic understanding of the role of behavior in infectious disease dynamics and mathematical modeling tools that are capable of accounting for those mechanisms, with the ultimate goal of enabling more effective use of public health interventions. The project will be grounded in empirical social science research in Ibagué, a city in Colombia with one of the highest urbanization rates and Aedes-borne disease transmission rates in the country. A combination of observational and experimental approaches will be used to characterize heterogeneity in the adoption of mosquito prevention behaviors in and around the home and to understand the cues that drive the adoption, or neglect, of those behaviors. These empirical findings will be used to develop a mathematical model of individual decision-making around the use of mosquito prevention behaviors in response to individual-level behavioral dispositions that change over time as cues arise and subside. This decision-making model will then be incorporated into an agent-based model of Aedes-borne disease transmission that will be used to infer the effectiveness of behavioral interventions that public health officials use to control Aedes-borne diseases in Ibagué. Finally, a suite of simpler macroscopic models will be developed and assessed with respect to their ability to capture effects of behavioral interventions on epidemiological dynamics simulated with the agent-based model. The ultimate outcome of the project will be the development and validation of minimally complex mathematical models that are capable of predicting responses of epidemiological dynamics to behavioral interventions.This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS), the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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": "12482", "attributes": { "award_id": "2312317", "title": "Collaborative Research: Mesoscale Predictability Across Climate Regimes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Physical & Dynamic Meteorology" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 10398, "first_name": "Kristen", "last_name": "Rasmussen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The prediction of severe weather such as tornadoes, large hail, and flooding continues to improve, allowing weather forecasts to better help society prepare for dangerous and damaging storms. Much of this improvement has come through understanding the causes of severe thunderstorms using models that simulate large portions of the atmosphere in detail, a procedure that requires the speed and performance of modern-day computers. Such computational capability allows the creation of multiple forecasts instead of just one for a given storm situation, highlighting the features in the atmosphere – like the degree of moisture or the wind profile – that lead to storms of different severity. These simultaneous forecasts also reveal how likely it is that upcoming storms may be severe, based on whether the different forecasts all agree on severe conditions (high likelihood of a severe event) or if forecasts show storms with a wide range of magnitudes (lower likelihood of a severe event). While these research methods have focused on understanding and improving severe storm prediction on a day-to-day basis, the predictability of high-impact weather events in a changing climate is unclear. The research aims to understand whether severe storms and their associated hazards can be better predicted as Earth's climate warms. This research is unique in that it goes beyond other studies that seek to uncover whether severe storms will become more or less frequent, instead determining if they are more or less predictable, a characteristic linked to the general atmospheric conditions that different climates support. The work will be performed by creating and analyzing big datasets of numerical weather model forecasts of severe storms in both recent (end of 20th century) and future (end of 21st century) climates. Specifically, how and why forecasts for severe storm situations evolve differently in different centuries will be assessed to understand the role climate change plays in atmospheric prediction.There are numerous expected impacts of this work on the scientific community and society. Understanding if probabilistic forecasts of severe storms will have increased or decreased uncertainty could show whether such forecasts could be used effectively in societal applications. One such example includes water reservoir operations, which rely on accurate predictions of flood risk to efficiently manage water resources. If flooding were to become more predictable, applications like this that benefit from forecast certainty could become more common, substantially helping regional water supply and mitigating the negative consequences of climate change in areas that become drier. The research will also involve the creation of a large dataset of severe storm-resolving simulations, allowing scientists who wish to analyze the data to investigate other aspects of severe storm-climate relationships beyond that suggested here. Several graduate and undergraduate students will be involved in the research in several ways: graduate and undergraduate research and dissemination through journal articles, academic coursework, and presentations at university symposia and professional scientific conferences. The general public and K-12 students in communities surrounding the participating universities will also benefit from planned outreach events including weekend events at university museums, university-sanctioned summer camps, and open house events that promote 1-on-1 interaction in casual environments with project scientists.This project is jointly funded by the Climate and Large-Scale Dynamics and Physical and Dynamic Meteorology programs in the Division of Atmospheric and Geospace Sciences as well as the Division of Atmospheric and Geospace Sciences to support projects that increase research capabilities, capacity and infrastructure at a wide variety of institution types, as outlined in the GEO EMBRACE DCL.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": "12483", "attributes": { "award_id": "2232999", "title": "Research: Do Test-Optional Policies in Engineering Admissions Improve Decision Making and Equity? Empirical Research using Experimental Simulations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EngEd-Engineering Education" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28420, "first_name": "Michael", "last_name": "Bastedo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "In the future, as test-optional admissions policies become more widespread in the post-COVID era, there are increasing claims about their potential to enhance student diversity. This study aims to explore critical policy and practice questions in this evolving landscape. Among various tools in admissions policies, the admissions office plays a significant role in granting access to engineering careers. However, there is a lack of research investigating the actual functioning of test-optional policies in the admissions process and how effectively they contribute to equitable outcomes. Therefore, the primary goal of this project will be to enhance decision making and implementation of test-optional policies in engineering admissions. By gaining insights into their impact, we aim to improve the admissions process for both applicants and admissions officers, fostering greater opportunities for aspiring engineering students. The research directly aligns with the National Science Foundation's focus on research in the formation of engineers, particularly research on the transition from high school to college and diversifying pathways to and through engineering degree programs. The goal of the project will be to examine the impact of test-optional admissions on the entry of highly-qualified and underrepresented students in engineering. Despite the recent pervasiveness of test-optional policies, empirical and experimental work remains needed to figure out how cognitive biases may prevent underrepresented students from being admitted to engineering programs. Thus, the research questions of this project will be as follows: 1) How does the submission of SAT/ACT test scores (or not) affect engineering admissions officers’ assessments of admissions files and decisions?; 2) Are there differential effects of acceptance rates related to test submissions for female, low-income students, and underrepresented racial groups?; 3) Does test submission influence engineering admissions officers differently depending on institutional selectivity, history of test-optional admissions, their demographic backgrounds, or their experience in the field? Participation in the study will involve actual college admissions officers, many of whom will have years of experience reviewing thousands of applications, and an experimental design approach will be utilized. In particular, we will use simulated admissions application files to investigate whether students who do not submit test scores under test-optional admissions policies will be disadvantaged by doing so, particularly for low-income students, women, and students of color underrepresented in engineering. This research will contribute to our understanding of how test-optional policies actually work in admissions practice and how well those processes will yield equitable outcomes. Furthermore, this project will have the potential to inform not only practices in engineering admissions, but also the activities of important national actors and partnerships.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": "12484", "attributes": { "award_id": "2243530", "title": "Measuring and Changing STEM Teacher Stress to Promote Effectiveness and Retention", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Robert Noyce Scholarship Pgm" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28421, "first_name": "Jeremy", "last_name": "Jamieson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The project aims to serve the national need of preparing and retaining effective mathematics teachers through its focus on understanding and addressing the crisis of STEM teacher stress in high need districts. In an era of elevated levels of clinically significant mental health problems linked to stress, mathematics teachers are tasked with the demands of overcoming learning losses due to the COVID-19 pandemic. Many teachers report this stressful demand is more than they have been prepared to handle. This stress can cause teachers to be less likely to use the most effective, student engagement teaching practices, and it can cause burnout that leads teachers to leave the profession. Issues related to stress, teacher effectiveness, and burnout may be elevated for pre-service teachers who teach in high-need school districts. Unfortunately, teacher preparation programs and school and district administrators are underprepared to respond to this crisis. Moreover, there are currently no validated, scalable, actionable measures that can help them to anticipate or respond to teachers’ stress. Therefore, this Noyce research project will first, as a preliminary matter, create a validated, scalable measure of teacher stress. Using wearable photoplethysmography sensors, data will be collected from 100 pre-service mathematics teachers and 100 in-service mathematics as part of the development of the stress measure. The measure will then be used to test novel hypotheses about ways to promote STEM teacher effectiveness and retention in high-need school districts. This project at the University of Texas at Austin and Rochester University includes partnerships with the UTeach teacher preparation program and two high-need school districts (Judson ISD and Round Rock ISD). Project goals include: (1) developing and validating a scalable measure of in-service and pre-service mathematics teachers’ levels of threat-type (versus challenge-type) stress, rooted in the biopsychosocial (BPS) model of challenge and threat; (2) testing a path model linking mathematics teachers’ daily threat-type stress responses to teacher effectiveness (implementation of deeper learning practices) and retention; and (3) identifying the administrator-level, classroom-level, and teacher-level factors that are most associated with teachers’ threat-type (vs. challenge-type) stress responses, and that are therefore potential targets for intervention. The intellectual merit of the project comes from its application of insights from the most recent scientific advances in the affective science of stress to the problem of mathematics teacher stress and coping, and the identification of ambulatory (sensors/monitors) and passive measures of teacher stress responding. Measures of stress developed and validated in the project will map teachers’ stress responses onto classroom teaching and learning processes, and investigate teacher retention and effectiveness. The broader impacts come from practical insights about how to improve teacher retention and effectiveness in high-need districts by addressing STEM teacher stress, and from the development of measures and scoring algorithms that can be used widely by researchers and practitioners. This Track 4: Noyce Research project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts.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": "12485", "attributes": { "award_id": "2327211", "title": "Multiscale data geometric networks for learning representations and dynamics of biological systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28422, "first_name": "Guy", "last_name": "Wolf", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "Recent years have seen significant volumes of high throughput, high dimensional, biomedical data arising from single cell sequencing technologies. Further, in contrast to past data of this type, current practices involve the collection of multiple single cell datasets (e.g., for each patient in a large cohort), which can represent data over time or in different conditions and can be analyzed computationally as point clouds. There is a great need for new mathematical and machine learning techniques to be able to process these types of complex data to gain meaningful and predictive insight on healthy and disease processes. While the majority of machine learning techniques used in the biomedical domain have been supervised techniques arising from language or vision (image) models, this project focuses on developing multiscale geometric and topological representations of more complex data structures. Such representations allow us to combine advances in several fields at the forefront of data science, including geometric deep learning, manifold learning, and harmonic analysis in order to analyze and predict from this data in an interpretable way. The research will include several biomedical applications, such as characterizing immune response in COVID-19, tracking the progress of metastatic cancer, predicting the effectiveness of immunotherapy, and understanding differentiation. Furthermore, the challenges addressed by these methods will enable new advances in a wide range of fields where complex high throughput data is collected in varying experimental environments. The project will provide representation learning techniques to explore and featurize high dimensional and complex datatypes including point clouds, graphs collected in a variety of conditions in order to perform machine learning tasks. Thrust 1 will involve the development of data geometric features to characterize point cloud data, based on which a novel class of neural networks will be created for regression on single cell data from a variety of systems. Thrust 2 will focus on methods for preserving directed information, by constructing asymmetric kernels and using these kernels for embedding, inference, and feature prediction. This will lead to the creation of directed graph neural networks that utilize geometric scattering as defined on a directional graph Laplacian of point cloud data. This will be used to learn and process data from gene regulatory and metabolic networks. Thrust 3 will focus here on inferring dynamics for interpolation of continuous dynamics from static snapshot single cell data using optimal transport-regularized neural ODEs and PDEs and producing interpretations of the underlying generative models. Further, it will involve representing dynamics quantitatively using data geometry and topology for prediction and classification, and will be validated on cancer and calcium signaling data from epithelial cells. The techniques developed here will provide fundamental advances in the use of neural networks to represent and make predictions on point cloud data, as well as enable new ways to tackle the problem of tracking dynamic biological processes over them.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": "12486", "attributes": { "award_id": "2318301", "title": "Collaborative Research: HSI Implementation and Evaluation Project: Developing a Wastewater-based Epidemiology Student Training and Education Program at CUNY", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28423, "first_name": "Olga", "last_name": "Calderon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 367, "ror": "https://ror.org/01d03cj21", "name": "Research Foundation of The City University of New York", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Track 2 project aims to establish a wastewater-based epidemiology training program at The City University of New York to respond to declining minority student retention and graduation rates in STEM. These declines hamper workforce development in industries clamoring for STEM talent and ultimately U.S. competitiveness in emerging technologies. The project will train students with wastewater-based epidemiology technologies and competencies, preparing the next generation of workers to face challenges posed by emerging infectious diseases. It is hypothesized that inspiring community college students with career prospects in an emerging technology, such as wastewater-based epidemiology, and providing academic, research, and social mentoring from role models that they can identify with will improve student retention, graduation rates, and career success. The program will generate interest in wastewater-based epidemiology as a career option, thus enhancing US pandemic preparedness. The program will also have ripple effects in different areas of society, such as water management, healthcare, public recreation and health, regulatory policy, science education, and economic mobility for graduates of the program and their families. By participating in the training program, students will be well-positioned for high-paying jobs in the public and private sectors or for graduate school.The specific aims of the project are: Aim 1- Advance the effectiveness of STEM education and workforce development programs, activities, and outreach through evaluation and assessment; Aim 2- Attract and train the Nation’s future STEM workforce through multiple pathways to educational and career opportunities; Aim 3- Increase participation of underserved and underrepresented groups in STEM education and workforce development programs, activities, and outreach; and Aim 4- Inspire community engagement in STEM education programs and activities to provide meaningful wastewater-based epidemiology learning opportunities for students and educators. The evaluation will include the use of surveys, interviews, observations, and career tracking to determine the impact of the program on students. The combination of pedagogical activities and hands-on experiential education, including internships and job training, will provide students with opportunities to commence STEM careers following graduation. The results of our work will be disseminated through publications, conference presentations, and through CUNY internal networks. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims.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": "12487", "attributes": { "award_id": "2318739", "title": "AGS-PRF: Novel Detection of Accelerated Emissions of Nitrogen Oxides Following High Latitude Peat Fires", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Postdoctoral Fellowships" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28424, "first_name": "Alan", "last_name": "Gorchov Negron", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2109, "ror": "", "name": "Gorchov Negron, Alan", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "In this Postdoctoral Research Fellowship project, the PI will use remote sensing observations along with laboratory and field measurements to determine if peatland fires, and the associated nitrogen emissions, might fertilize nearby high-latitude soils resulting in an enhancement of microbial production of nitrogen oxides. These northern high-latitude soils are the largest reservoir of soil organic matter on the planet, and ongoing disruptions due to wildfire and other climatic changes could result in a substantial release of greenhouse gases and reactive gases such as nitrogen oxides, which are the focus of this work. The new knowledge gained during this project could enhance our understanding of an understudied and potentially substantial source of soil nitrogen emissions, which will be beneficial to atmospheric chemistry and climate research communities, as well as the broader public.The specific tasks include: (1) using satellite remote sensing data to document trends and emissions of nitrogen dioxide and carbon monoxide from high-latitude fires and soils; (2) conduct laboratory soil chamber experiments to quantify in-situ emissions from burning peatland soils; (3) deploy a network of nitrogen oxides analyzers to compare ambient nitrogen oxides concentrations nearby both burned and pristine soils; (4) combine these data with forward modeling in order to compare to satellite columns; and (5) conduct additional laboratory experiments to determine physical and chemical controls of soil nitrogen oxides. The end result will provide emission factors of nitrogen oxides from fires and soils in high-latitude land masses and will provide information that might explain the nitrogen oxides anomaly observed after peatland fires. The PI plans on joining and enhancing the Polar Science Early Career Community Office and mentoring an undergraduate student during the course of this project.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": "12488", "attributes": { "award_id": "2327797", "title": "Collaborative Research: IHBEM: The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28425, "first_name": "Shengjie", "last_name": "Lai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "When people change where, when, and why they travel, there are effects on infectious diseases. People’s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people’s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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": "12489", "attributes": { "award_id": "2327798", "title": "Collaborative Research: IHBEM: The fear of here: Integrating place-based travel behavior and detection into novel infectious disease models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28426, "first_name": "Robert", "last_name": "Holt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "When people change where, when, and why they travel, there are effects on infectious diseases. People’s movements determine who is at risk of the disease and whether new cases are counted by local public health agencies. For example, during the COVID-19 pandemic, people’s movements changed drastically and, in addition to COVID-19, influenza and Lyme disease cases also dropped nationwide. These drops in cases may be because people spent less time in high risk areas, or simply because people traveled to healthcare facilities less frequently, and so fewer cases are reported. Distinguishing between these alternatives is critical for understanding disease control and predicting disease spread, but is made difficult when travel patterns change dramatically. This problem is especially challenging because communities may modify travel patterns in response to local disease, which can, in turn, change how diseases spread in communities and how public health monitors disease. To determine the cause of case reductions as human movements changed, the Investigators will develop new mathematical models that account for the ways travel impacts both risk and detection, using data from mobile phones to inform transmission risk and using local surveys to inform underdetection rates. By developing this new collection of models, the Investigators will better understand how transmission and detection of various non-COVID-19 infections changed throughout the pandemic, recognize how this depends on the biology of the disease being considered, and predict how case numbers may change during future periods of significant community-level changes in travel.Community-level travel patterns have multifactorial effects on the dynamics of any infectious disease. Major changes to travel patterns affect both transmission, as people spend more or less time in high-risk places, and detection, as people change their propensity to visit healthcare facilities. These factors also influence individual behaviour, because local increases in reported cases can cause people to change their travel further. This creates critically important feedback loops between transmission, detection, and travel. Depending on the interactions between these factors, changes to travel or transmission could lead to undercounting of cases or a harmful population-level response that leads to communities being exposed to more infections. As changes in community-level travel patterns become more likely with global factors such as climate change and emerging infectious disease threats, it becomes increasingly important for models to integrate their effects on both detection and transmission. The project addresses this need by developing novel models that account for the ways in which travel can simultaneously affect both transmission and detection, and be affected by reported and perceived disease risk. The Investigators will combine the models with mobility data obtained from SafeGraph and use local surveys to inform underdetection rates of key notifiable diseases across the New River Valley Health District of Virginia, and to develop a framework for predicting transmission and detection changes during future large-scale changes in travel. Central Appalachia is a key region for this work, as it experiences relatively high incidence of respiratory and Lyme diseases, and intervention adherence was especially low during the later stages of the COVID-19 pandemic. This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1405, "pages": 1424, "count": 14236 } } }