Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&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=1392&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=-principal_investigator" }, "data": [ { "type": "Grant", "id": "397", "attributes": { "award_id": "2150651", "title": "Collaborative Research: Building Capacity for Cross-site Research on Promoting Noticing for Equity and Equitable Science Teaching Practice through Video Analysis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 747, "first_name": "Kathleen", "last_name": "Bergin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2023-06-30", "award_amount": 31500, "principal_investigator": { "id": 748, "first_name": "Michelle", "last_name": "Forsythe", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 204, "ror": "", "name": "Texas State University - San Marcos", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 204, "ror": "", "name": "Texas State University - San Marcos", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The project will serve the national need to develop equitable and effective science teaching practice by building capacity to investigate the use of video analysis tasks in teacher preparation. Through a cross-site collaboration, a plan for a longitudinal study focused on STEM teacher effectiveness and retention in high-need schools will be developed. The COVID-19 pandemic gives new urgency to video analysis as large numbers of teacher preparation programs have had to use digital libraries of classroom video cases in lieu of traditional field experiences in schools. In addition, the importance of how teachers are prepared to enact equitable, justice-oriented, culturally responsive, and linguistically sustaining pedagogies in support of the success of K-12 students in science, technology, engineering, and mathematics education cannot be underestimated. This project builds upon five years of prior work of the seven institutions involved in which the partners discussed, designed, and studied video analysis within science teacher preparation and developed the Framework for Analyzing Video in Science Teacher Education (FAVSTE).This project involves a collaboration of science teacher educators from partnering universities (the Collaborative) including Vanderbilt University, Texas State University—San Marcos, Teachers College—Columbia University, West Chester University of Pennsylvania, the University of Northern Iowa, Florida International University, and Kennesaw State University who will work with partner high-need school districts. The project has three main intents regarding capacity building intended to situate the Collaborative to be well-positioned to submit a Track 4 Research proposal. One is to continue to analyze and modify the FAVSTE framework and associated tools to ensure that they explicitly support noticing for equity. A second is to identify, modify, and pilot research instruments to analyze teachers’ professional vision, its link to equitable science teaching practice, and how this vision changes over time in relation to teacher effectiveness and retention. And the third is to develop a cross-site, longitudinal research study that incorporates practical instructional tools for video analysis and research tools for studying effectiveness and retention of STEM teachers in high-need school districts. Data collection and analysis consistent with a design-based research approach will be used. The work will allow the Collaborative to deepen its theoretical and methodological understanding of how to use video in teacher preparation to support equitable science teaching practice. The project intends to disseminate insights and best practices emanating from this project through the National Board for Professional Teaching Standards (NBPTS), a partner of the Collaborative, as well as professional organizations including the Association for Science Teacher Education (ASTE). This Capacity Building 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 persistence, retention, and effectiveness 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": "396", "attributes": { "award_id": "2150652", "title": "Collaborative Research: Building Capacity for Cross-site Research on Promoting Noticing for Equity and Equitable Science Teaching Practice through Video Analysis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 745, "first_name": "Kathleen", "last_name": "Bergin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2023-06-30", "award_amount": 14119, "principal_investigator": { "id": 746, "first_name": "Anna Marie", "last_name": "Arias", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 203, "ror": "", "name": "Kennesaw State University Research and Service Foundation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 203, "ror": "", "name": "Kennesaw State University Research and Service Foundation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "The project will serve the national need to develop equitable and effective science teaching practice by building capacity to investigate the use of video analysis tasks in teacher preparation. Through a cross-site collaboration, a plan for a longitudinal study focused on STEM teacher effectiveness and retention in high-need schools will be developed. The COVID-19 pandemic gives new urgency to video analysis as large numbers of teacher preparation programs have had to use digital libraries of classroom video cases in lieu of traditional field experiences in schools. In addition, the importance of how teachers are prepared to enact equitable, justice-oriented, culturally responsive, and linguistically sustaining pedagogies in support of the success of K-12 students in science, technology, engineering, and mathematics education cannot be underestimated. This project builds upon five years of prior work of the seven institutions involved in which the partners discussed, designed, and studied video analysis within science teacher preparation and developed the Framework for Analyzing Video in Science Teacher Education (FAVSTE).This project involves a collaboration of science teacher educators from partnering universities (the Collaborative) including Vanderbilt University, Texas State University—San Marcos, Teachers College—Columbia University, West Chester University of Pennsylvania, the University of Northern Iowa, Florida International University, and Kennesaw State University who will work with partner high-need school districts. The project has three main intents regarding capacity building intended to situate the Collaborative to be well-positioned to submit a Track 4 Research proposal. One is to continue to analyze and modify the FAVSTE framework and associated tools to ensure that they explicitly support noticing for equity. A second is to identify, modify, and pilot research instruments to analyze teachers’ professional vision, its link to equitable science teaching practice, and how this vision changes over time in relation to teacher effectiveness and retention. And the third is to develop a cross-site, longitudinal research study that incorporates practical instructional tools for video analysis and research tools for studying effectiveness and retention of STEM teachers in high-need school districts. Data collection and analysis consistent with a design-based research approach will be used. The work will allow the Collaborative to deepen its theoretical and methodological understanding of how to use video in teacher preparation to support equitable science teaching practice. The project intends to disseminate insights and best practices emanating from this project through the National Board for Professional Teaching Standards (NBPTS), a partner of the Collaborative, as well as professional organizations including the Association for Science Teacher Education (ASTE). This Capacity Building 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 persistence, retention, and effectiveness 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": "395", "attributes": { "award_id": "2148501", "title": "Collaborative Research: Parenting, Housework, Well-being, and the COVID-19 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": 743, "first_name": "Melanie", "last_name": "Hughes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2025-03-31", "award_amount": 169747, "principal_investigator": { "id": 744, "first_name": "Daniel L", "last_name": "Carlson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 202, "ror": "https://ror.org/03r0ha626", "name": "University of Utah", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 202, "ror": "https://ror.org/03r0ha626", "name": "University of Utah", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has dramatically altered family life in the United States. This project studies how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and what factors may be driving these changes. It also investigates long-term consequences of the pandemic for the division of household labor between mothers and fathers, and the impacts of the pandemic on parents’ well-being. Starting in the first months of the pandemic and continuing for five years, this project illuminates how family and work life have changed over the course of the COVID-19 pandemic. Insights from this study inform decisionmakers to better meet the needs of working parents and families.This project uses longitudinal survey data from a sample of partnered U.S. parents. Parents were first surveyed in April 2020 and asked about their work and household activities prior to and one month after the pandemic began. These parents were surveyed again in November 2020 and October 2021, along with a new set of respondents at each wave. Follow-up surveys result in six waves of data spanning the period from March 2020 through September 2025. Approximately 6,500 parents are surveyed at least once during the study. These data are used to assess (a) changes in parents’ divisions of domestic labor, (b) the factors driving these changes, (c) effects on mothers’ labor force participation, and (d) changes in parents’ well-being and relationship quality. The novel nature of these data are uniquely situated to assess the long-term consequences of the COVID-19 pandemic for parents’ work and family life and their well-being.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": "394", "attributes": { "award_id": "2148610", "title": "Collaborative Research: Parenting, Housework, Well-being, and the COVID-19 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": 741, "first_name": "Melanie", "last_name": "Hughes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2025-03-31", "award_amount": 160253, "principal_investigator": { "id": 742, "first_name": "Richard J", "last_name": "Petts", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 201, "ror": "https://ror.org/00k6tx165", "name": "Ball State University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 201, "ror": "https://ror.org/00k6tx165", "name": "Ball State University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has dramatically altered family life in the United States. This project studies how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and what factors may be driving these changes. It also investigates long-term consequences of the pandemic for the division of household labor between mothers and fathers, and the impacts of the pandemic on parents’ well-being. Starting in the first months of the pandemic and continuing for five years, this project illuminates how family and work life have changed over the course of the COVID-19 pandemic. Insights from this study inform decisionmakers to better meet the needs of working parents and families.This project uses longitudinal survey data from a sample of partnered U.S. parents. Parents were first surveyed in April 2020 and asked about their work and household activities prior to and one month after the pandemic began. These parents were surveyed again in November 2020 and October 2021, along with a new set of respondents at each wave. Follow-up surveys result in six waves of data spanning the period from March 2020 through September 2025. Approximately 6,500 parents are surveyed at least once during the study. These data are used to assess (a) changes in parents’ divisions of domestic labor, (b) the factors driving these changes, (c) effects on mothers’ labor force participation, and (d) changes in parents’ well-being and relationship quality. The novel nature of these data are uniquely situated to assess the long-term consequences of the COVID-19 pandemic for parents’ work and family life and their well-being.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": "393", "attributes": { "award_id": "2217675", "title": "Conference: Society for Research on Biological Rhythms: From Molecules to Policy", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 739, "first_name": "Edda", "last_name": "Thiels", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2023-04-30", "award_amount": 22463, "principal_investigator": { "id": 740, "first_name": "Ilia", "last_name": "Karatsoreos", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 200, "ror": "https://ror.org/0072zz521", "name": "University of Massachusetts Amherst", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 200, "ror": "https://ror.org/0072zz521", "name": "University of Massachusetts Amherst", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Nearly all organisms have evolved molecular, cellular, physiological and behavioral adaptations to predict regular oscillations in the environment. As such, biological rhythms are a core component on life on Earth, and run the gamut from oscillations occurring on the scale of seconds, and those on the scale of the seasons. The study of biological rhythms over the past century has led to the discovery of an exquisite cellular and molecular timekeeping system, which impacts everything from physiology to behavior. Indeed, in 2017 a group of pioneers in our field were awarded the Nobel Prize for Physiology or Medicine for their groundbreaking work unravelling this molecular puzzle. While biological clocks are a fundamental aspect of life, their effects on a wide range of biological processes are finally being appreciated. Our growing understanding of how biological rhythms impact many areas of the life sciences is starting to inform how we think about public health, labor and education systems, and the consequences of the built environment. The objective of the Society for Research on Biological Rhythms (SRBR) 2022 meeting is to foster discussion from “molecule to policy”, and highlight the groundbreaking work of both senior and junior scientists. The activity includes expansion of the mentoring and training efforts that SRBR has worked hard to establish. Emphasis is placed on recruitment and retention of members from underrepresented minority populations and increasing the diversity of our community. Collaboration between experts in biological timing and other scientists, engineers, and policy makers is critical to increase awareness of the global impact of biological rhythms. The theme for the 2022 SRBR meeting, “From Molecule to Policy”, will directly embrace this challenge. Comparative approaches are a critical component of this meeting, with inclusion of both model and non-model organisms, as well as both laboratory and field studies, in microbes, plants, humans and in non-human animals. The program includes topics such as the neural networks of timekeeping, rhythms in bacteria and microbes, mathematical models of oscillatory systems, analysis strategies for rhythms in “Big Data”, chronotherapeutics, effects of rhythms on metabolism, and even the intersection of biological rhythms and the COVID-19 pandemic. The scientific program is inclusive, diverse, and timely. SRBR 2022 fosters and strives to maintain a diverse and inclusive scientific community. A key component of this proposal is the support of the day-long Trainee and Professional Development event, which includes dozens of formal workshops and training events targeted at trainees and junior faculty. In addition to formal and informal mentoring and training events during the conference, this day-long event sets the stage for trainees’ active participation in the meeting. SRBR 2022 promotes increased attendance of underrepresented groups and researchers from low-income countries through a variety of awards and fellowships, with the goal of increasing participation as well as improving retention.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": "392", "attributes": { "award_id": "2223710", "title": "Towards a green and inclusive post-pandemic recovery of the Blue Economy and coastal communities", "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": 737, "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": "2022-06-01", "end_date": "2025-05-31", "award_amount": 200000, "principal_investigator": { "id": 738, "first_name": "Marta", "last_name": "Vicarelli", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 200, "ror": "https://ror.org/0072zz521", "name": "University of Massachusetts Amherst", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 200, "ror": "https://ror.org/0072zz521", "name": "University of Massachusetts Amherst", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This research project uses case studies from coastal communities in four countries---Costa Rica, Germany, Scotland (UK), and the United States---to study the environmental and economic impacts of the COVID-19 pandemic on these communities. These communities depend on coastal communities that depend on coastal tourism (Blue Economy) for their livelihoods, but the pandemic led to drastic reduction in tourism and other economic activities in these communities. The project assesses which aspects of the Blue Economy communities prefer to recover; determinants of resilient and inclusive development of coastal communities; and determinants coastal communities that depend on coastal tourism (Blue Economy) for their livelihoods of socioeconomic performance. In addition, the project involves the training of early career researchers. The research pays particular attention to the resilience of vulnerable people, and possible strategies for recovery. The results of this research project provide inputs into policies to mitigate the negative consequences of the COVID-19 pandemic on communities living in environmentally sensitive coastal communities and the development of resilient and fair post-pandemic development of coastal economies. The results of this research could therefore help to establish the U.S. as a global leader in environmental and climate friendly development.This research project uses several methods and a comparative approach to study the environmental and socioeconomic impacts of the COVID-19 pandemic, especially on coastal economies that depend on environmental tourism (Blue Economy). Specifically, the project studies the socioeconomic impacts of COVID-19 and policy response to the pandemic; short- and long-term COVID-19 recovery strategies of the Blue Economy; barriers to coastal green and inclusive recovery policies; elements of successful resilience recovery strategies; and lessons and best practices transferable across risks and locations to efficiently respond to or prevent future crises. The project employs large-scale surveys of coastal regional authorities and businesses, discrete choice experiments to value future recovery scenarios, and expert interviews. The results of this research are shared with other academic institutions, partners, and key stakeholders in each country. The results of this research project provide inputs into policies to mitigate the negative consequences of the COVID-19 on communities living in environmentally sensitive coastal communities and the development resilient and fair post-pandemic development of coastal economies. The results of this research therefore help to establish the U.S. as a global leader in environmental and climate friendly development.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": "391", "attributes": { "award_id": "2153901", "title": "Collaborative Research: A Control Theoretic Framework for Guided Folding and Unfolding of Protein Molecules", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 735, "first_name": "Harry", "last_name": "Dankowicz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2025-07-31", "award_amount": 220952, "principal_investigator": { "id": 736, "first_name": "Mark W", "last_name": "Spong", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This grant will fund research that enables accurate prediction of pathways for protein folding and unfolding, with application to computer-aided anti-viral drug design, control of protein-based nano-machines, and treatment of diseases related to protein misfolding such as Alzheimer’s, thereby promoting the progress of science, and advancing the national health and prosperity. Physics-based approaches reliably capture the processes that govern conformational changes of protein molecules, but typically do so at great computational expense. A recently developed modeling paradigm, which describes protein molecules in terms of large numbers of rigid nano-linkages that fold under the influence of interatomic forces, can significantly reduce the computational burden, but presents challenges with ensuring that the predicted folding and unfolding pathways are realistic and not artificially driven by the numerical algorithm. In this project, this challenge is overcome using an optimization-based control theoretic framework to guide both folding and unfolding dynamics while respecting biologically realistic rates of change of conformational entropy. Knowledge gained from the development of this framework will enable systematic investigation of protein conformational dynamics, including unfolding pathways of coronavirus spike proteins, while also advancing previously unexplored control tools that may help robots navigate cluttered environments. A unique approach to sonification of protein pathway data will make this knowledge broadly accessible and will be integrated in course projects for undergraduate students in engineering, computer science, and art, as well as in research activities aiming to mentor high school students in STEM.This research aims to bridge the two seemingly unrelated fields of optimization-based nonlinear control and conformational dynamics of proteins through rigorous development and investigation of computationally efficient and numerically stable algorithms that accurately predict protein folding and unfolding while avoiding pathways associated with artificially rapid loss of conformational entropy. This project will fill the critical gap in knowledge of encoding entropy-loss constraints using the kinetostatic compliance method by developing a novel non-iterative, large-scale, quadratic programming-based control scheme over hyper-ellipsoids for protein folding dynamics with large state-space dimensions; constructing a large-scale, variable-step-size, numerical integration algorithm that is expected to reduce the number of integration steps, where each step requires the burdensome computation of a very large interatomic force vector field; and developing a control theoretic approach for systematically investigating the problem of protein unfolding. Ground truth data for validation will be obtained from all-atom molecular dynamics simulations and, in the case of the model protein barnase, publicly available experimental data from optical tweezer-based mechanical unfolding experiments.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": "390", "attributes": { "award_id": "2153744", "title": "Collaborative Research: A Control Theoretic Framework for Guided Folding and Unfolding of Protein Molecules", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 733, "first_name": "Harry", "last_name": "Dankowicz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2025-07-31", "award_amount": 266578, "principal_investigator": { "id": 734, "first_name": "Alireza", "last_name": "Mohammadi", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 198, "ror": "", "name": "Regents of the University of Michigan - Dearborn", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 198, "ror": "", "name": "Regents of the University of Michigan - Dearborn", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This grant will fund research that enables accurate prediction of pathways for protein folding and unfolding, with application to computer-aided anti-viral drug design, control of protein-based nano-machines, and treatment of diseases related to protein misfolding such as Alzheimer’s, thereby promoting the progress of science, and advancing the national health and prosperity. Physics-based approaches reliably capture the processes that govern conformational changes of protein molecules, but typically do so at great computational expense. A recently developed modeling paradigm, which describes protein molecules in terms of large numbers of rigid nano-linkages that fold under the influence of interatomic forces, can significantly reduce the computational burden, but presents challenges with ensuring that the predicted folding and unfolding pathways are realistic and not artificially driven by the numerical algorithm. In this project, this challenge is overcome using an optimization-based control theoretic framework to guide both folding and unfolding dynamics while respecting biologically realistic rates of change of conformational entropy. Knowledge gained from the development of this framework will enable systematic investigation of protein conformational dynamics, including unfolding pathways of coronavirus spike proteins, while also advancing previously unexplored control tools that may help robots navigate cluttered environments. A unique approach to sonification of protein pathway data will make this knowledge broadly accessible and will be integrated in course projects for undergraduate students in engineering, computer science, and art, as well as in research activities aiming to mentor high school students in STEM.This research aims to bridge the two seemingly unrelated fields of optimization-based nonlinear control and conformational dynamics of proteins through rigorous development and investigation of computationally efficient and numerically stable algorithms that accurately predict protein folding and unfolding while avoiding pathways associated with artificially rapid loss of conformational entropy. This project will fill the critical gap in knowledge of encoding entropy-loss constraints using the kinetostatic compliance method by developing a novel non-iterative, large-scale, quadratic programming-based control scheme over hyper-ellipsoids for protein folding dynamics with large state-space dimensions; constructing a large-scale, variable-step-size, numerical integration algorithm that is expected to reduce the number of integration steps, where each step requires the burdensome computation of a very large interatomic force vector field; and developing a control theoretic approach for systematically investigating the problem of protein unfolding. Ground truth data for validation will be obtained from all-atom molecular dynamics simulations and, in the case of the model protein barnase, publicly available experimental data from optical tweezer-based mechanical unfolding experiments.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": "389", "attributes": { "award_id": "2145962", "title": "CAREER: Rational Design of One-Dimensional Contacts to Two-Dimensional Atomically Thin Heterostructure for High-Performance and Low Noise Field Effect Transistors and Biosensors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 731, "first_name": "Vikram", "last_name": "Dalal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2027-02-28", "award_amount": 500000, "principal_investigator": { "id": 732, "first_name": "Suprem R", "last_name": "Das", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 197, "ror": "https://ror.org/05p1j8758", "name": "Kansas State University", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 197, "ror": "https://ror.org/05p1j8758", "name": "Kansas State University", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "Field Effect Transistors (FETs) with two-dimensional atomically thin materials and one-dimensional contacts are studied in this project. These transistors will demonstrate high performance and low noise characteristics and will lay the foundation for emerging technologies including biosensor devices and circuits. Though silicon-based electronics have been the basis for innovation for several decades, their performance at atomically thin dimensions breaks down due to the bottleneck in its intrinsic crystal symmetry and associated physical and chemical properties. This project investigates the use of graphene, hexagonal boron nitride, and transition metal dichalcogenides to form atomically thin FETs. Given their unprecedented physical properties such as material and device tunability and energy efficiency at the atomic level, these devices will revolutionize the use of FETs in new applications that will transform electronics across industries such as communications, healthcare, and environmental sensing. It is expected that these devices and circuits that deploy them will play a great role in the development of internet-of-things (IoT), Industry 4.0, data analytics, artificial intelligence, and machine learning. This project will train next generation researchers, including women scientists and engineers and those from underrepresented populations, in micro-nanoscale science and engineering to gain expertise in addressing some of the complex societal problems such as creating sensors for environmental monitoring to disease diagnostics. The results obtained from this project will be integrated in educational activities. The proposed research aims to demonstrate high performance and low noise field effect transistor devices that will uniquely be used as a platform for highly sensitive biosensors. The objectives of the proposal is to rationally design to study correlated electrical transport and noise phenomena in number of two dimensional atomically thin heterostructure field effect transistors involving graphene, hexagonal boron nitride, and transition metal dichalcogenides (such as molybdenum disulfide) by understanding (1) contact engineering with low work function metals and semimetals as source and drain electrodes, (2) edge contacted architectures with large transfer length and low contact resistance, (3) manipulating polar phonons by dielectric engineering using isotopically pure hexagonal boron nitrides and (4) development of antibody/antigen immunosensors with high sensitivity for SARS-CoV-2. Key to all these successes requires a fundamental understanding in material tunability in atomic scale, quantum confinement and relation to band structure, device architecture and integration, and correlated phenomena between electrical transport and noise physics. Based on experimental design parameters and results (such as a two probe vs. four probe measurement design), new device models beyond the traditional silicon transistor and biosensor model will be developed. Quantum confined physics will be exploited to demonstrate their structure and property tunability and their relation to the functionality (e.g., sensing characteristics).This project is jointly funded by ECCS and the 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": "388", "attributes": { "award_id": "2225607", "title": "The 48th Northeast Bioengineering Conference", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 729, "first_name": "Stephanie", "last_name": "George", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2022-10-31", "award_amount": 15000, "principal_investigator": { "id": 730, "first_name": "Qi", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This award supports the 48th annual Northeast Bioengineering Conference (NEBEC), a regional conference that is historically student-centered. The conference will be held at Columbia University, New York City, New York, April 23-24, 2022. This is the first in-person NEBEC conference after the COVID pandemic and provides an opportunity to undergraduate and graduate student researchers in the field of biomedical engineering to meet and present their research to their peers. The conference has been impactful because it allows students in the Northeast region who do not have the funding to attend a national biomedical engineering meeting to present their work to a broad audience. This opportunity will encourage students to pursue graduate school and pursue a STEM career. The award will support a reduction in registration fees for undergraduate students and postdoc fellows, thus making the conference even more affordable to early career scientists, especially financially disadvantaged students. As a conference with a strong tradition for educating the next generation of undergraduate students, graduate students, and postdoctoral fellows in biomedical engineering, the 48th Northeast Bioengineering Conference at Columbia University will bring together a number of leaders in various bioengineering fields to inform and inspire students about progress and research opportunities in the biomedical engineering. The conference expects to attract more than 450 attendees from around the Northeast region of the United States, with 60% of which expected to be students. The Biomedical Engineering Society and IEEE Engineering in Medicine and Biology Society Student Chapters at different universities in the Northeast region are reaching out and encouraging participation by students, especially female and under-represented minority students from the region. Young Scientist Awards and New Innovator Award will be given to specifically support professional development of early career scientists. Themes of the presentations will include the following: Neural Engineering, Cellular and Molecular Bioengineering, Biomechanics, Tissue Engineering, Synthetic Biology, Computational Biology/Bioinformatics, Medical Devices, and Biophotonics / Biomedical Imaging. Additional sessions planned include a senior design competition, Future Bioengineer session, and a panel discussion about biomedical engineering education and research after COVID.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": 1384, "pages": 1392, "count": 13920 } } }