Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10014", "attributes": { "award_id": "2225554", "title": "RECODE: Defining Environmental Design Criteria for Directed Differentiation of Type 1 from Type 2 Lung Alveolar Epithelial Cells", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "RECODE" ], "program_reference_codes": [], "program_officials": [ { "id": 847, "first_name": "Steve", "last_name": "Zehnder", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2025-09-30", "award_amount": 1500000, "principal_investigator": { "id": 25839, "first_name": "Daniel", "last_name": "Weiss", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25836, "first_name": "Chelsea M", "last_name": "Magin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25837, "first_name": "Amy L", "last_name": "Ryan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25838, "first_name": "Bradford J", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 433, "ror": "", "name": "University of Vermont & State Agricultural College", "address": "", "city": "", "state": "VT", "zip": "", "country": "United States", "approved": true }, "abstract": "Injury to the lungs can have devastating consequences, as exemplified by the recent COVID19 pandemic. However, the cellular and molecular mechanisms by which the lung repairs itself remain poorly understood. The objective of this RECODE project is to utilize novel and sophisticated bioengineering approaches to better define cell and molecular pathways underlying lung development and repair. The project focuses on cells involved in the major function of the lung: gas exchange which provides oxygen to the body. This information will inform the development of new tunable biomaterials to guide lung cell development. The research efforts of this RECODE project are integrated with educational and outreach objectives to promote active learning in biomedical engineering and biologic sciences undergraduates, to develop outreach programs to encourage and inspire local high school science, engineering, and mathematical sciences students by hosting educational workshops poster sessions, and to promote biomedical engineering research and education towards the general public at each of the participating sites in Vermont, Colorado, and Iowa.\n\nThere remains a critical need for better understanding of fundamental cellular and molecular mechanisms of lung development and repair, particularly with respect to the alveolar epithelium, a fundamental component of gas exchange. Current in vitro model systems, including organoid cultures, have provided important information but fail to fully reproduce native tissue structure or relevant environmental influences such as extracellular matrix (ECM) composition or stiffness. The central vision of this RECODE project is to devise and validate a robust system for delineating the mechanisms by which ECM composition and stiffness regulate differentiation of alveolar type 2 epithelial cells (AT2s) to alveolar type 1 epithelial cells (AT1s). Utilizing AT2s derived from human induced pluripotent stem cells (iAT2s), sophisticated tissue engineering approaches incorporating hydrogels derived from alveolar-enriched regions (aECM) of decellularized human lungs will be developed to evaluate effects of physiologically relevant ECM composition and stiffness on AT2 to AT1 directed differentiation. In silico modeling will be deployed in parallel to direct the empiric studies and to develop a holistic differentiation control framework. These approaches will be assessed in specific directed objectives: 1) To determine the specific ECM components regulating primary vs iAT2 stemness and driving AT1 differentiation; 2) To investigate the impact of dynamically tunable microenvironmental stiffness on primary vs iAT2 stemness and AT1 differentiation; and 3) To leverage agent-based and statistical modeling to predict combinatorial effects of composition and stiffness on primary vs iAT2 to AT1 differentiation. These unique and innovative approaches involve a multidisciplinary and multi-institutional combination of materials science, lung regenerative medicine, lung stem cell biology, and in silico modeling. Further, the paradigms and approaches generated will have broader impact and applicability to understanding cell-ECM interactions in enabling cell differentiation in a wider range of organ systems.\n\nThis RECODE project is jointly funded by the Engineering Biology and Health Cluster in the Division of Chemical, Bioengineering, Environmental, and Transport Systems, the Established Program to Stimulate Competitive Research (EPSCoR), and the Physiological Mechanisms and Biomechanics Program and Animal Developmental Mechanisms Program in the Division of Integrative Organismal Systems.\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": "10015", "attributes": { "award_id": "2213656", "title": "CCRI: Research Infrastructure: NEW: Semantic Scholar Open Data Platform: Enabling Research Into Scientific Search and Discovery", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CCRI-CISE Cmnty Rsrch Infrstrc" ], "program_reference_codes": [], "program_officials": [ { "id": 864, "first_name": "Sylvia", "last_name": "Spengler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2024-08-31", "award_amount": 2000000, "principal_investigator": { "id": 25841, "first_name": "Daniel", "last_name": "Weld", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25840, "first_name": "Oren", "last_name": "Etzioni", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1875, "ror": "", "name": "AI SQUARED", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "The exponential growth of scientific publication makes it difficult for scientists to track developments in their field and make connections between different advances. In response, artificial-intelligence researchers have started to develop techniques that allow computers to ‘read’ scientific papers and automatically classify topics, extract key results, summarize contributions, identify connections, and select a personalized set of papers that may be of special interest to each scientist. The enduring vision is to build AI systems that can process an immense corpus of scholarly documents and augment the capabilities of human scientists – accelerating scientific discovery and helping humanity quickly confront disasters such as the COVID-19 pandemic. The proposed Semantic Scholar Open Data Platform builds infrastructure to support this research by first gathering a comprehensive set of papers and arranging for efficient indexing. The system processes PDF-formatted papers to extract information and use advanced analytic processing approaches to provide researchers access to results. The infrastructure will dramatically lower the barrier to entry for newcomers to the field of scholarly document processing, improve reproducibility of experiments, and accelerate innovation in the important area of AI-augmented scientific discovery\n\nThe infrastructure proposed is unique, because alternative sources of academic papers are either closed, incomplete, have limited programmatic access, or have been retired. The proposed Semantic Scholar Open Data Platform has three parts: 1) a comprehensive set of online services enabling researchers to programmatically search, filter, extract, summarize, and analyze a large and continually-updated corpus of documents; 2) a new mechanism that enables researchers to curate their own domain-specific text corpora, as the team previously created the CORD-19 dataset for coronavirus research; 3) open source software, including pretrained language models and user interface templates to serve as research building blocks. Together the infrastructure will dramatically lower the barrier to entry for newcomers to the field of scholarly document processing, improve reproducibility of experiments, and accelerate innovation in the important area of AI-augmented scientific discovery. Fortunately, the recent increase in research in scholarly document processing (e.g., the rapid uptake of our CORD-19 dataset) shows that the computer and information science community has the interest and capability to develop new technologies that accelerate science and help meet global societal challenges, such as pandemics and climate change. The resulting advances in AI-augmented scientific discovery will benefit all areas of science, spurring medical advances, creating new jobs, and improving access for blind researchers. We will improve global infrastructure by providing open services, data sets, code, and associated educational materials. The team will also engage with underrepresented STEM students and through K-12 outreach.\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": "10016", "attributes": { "award_id": "2210722", "title": "Collaborative Research: PIC: Slow Wave Enhanced Electrooptically Tuned Michelson Interferometer Biosensor for On-Chip Dual Polarization Interferometry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 763, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2025-08-31", "award_amount": 160260, "principal_investigator": { "id": 25842, "first_name": "Bibhudutta", "last_name": "Rout", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 253, "ror": "https://ror.org/00v97ad02", "name": "University of North Texas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID pandemic of 2020 demonstrated the worldwide need for low-cost, highly sensitive, rapid\ndiagnostic testing of diverse pathogens. While silicon photonics enables such a highly multiplexed label-free\nsensing capability with extremely high sensitivities, a handheld low-cost silicon nanophotonic sensor\nis still missing. Fabrication imperfections have made photonic sensor implementations difficult with a fixed\nwavelength laser and a single detector. Photonic measurement variabilities also arise from binding\nuncertainties in nanophotonic pillars and trenches. The fundamental work in this proposal employs a novel\non-chip dual polarization interferometry technique that will reduce photonic measurement variability, and\nnovel circuit implementations to enable electrically driven and electrically readout low-cost on-chip\nnanophotonic sensors. The working principle of the device, and circuit implementations of the device to\novercome fabrication and measurement limitations have not been previously demonstrated. The state-of-the-\nart photonic device fabrication capabilities at a 300 mm CMOS foundry, namely AIM Photonics, with\nmonolithically integrated passive and active electrically biased photonic components will be employed in\nthis project. The project will involve students in optics, engineering, materials science, and physics from\nthe University of Dayton and the University of North Texas who will not only learn about cutting-edge\nSTEM (science, technology, engineering, and mathematics) research but also in computer aided design\nlayouts for foundry fabrication of next-generation co-integrated electronic-photonic devices. The project\nwill also work with students and faculty in microbiology from the Dayton Early College Academy, and\nother middle and high school students in the greater Dayton, OH and Denton, TX areas. The handheld\nsensors will find applications in various domains of biological sensing for cancer diagnostics, infectious\ndisease and opioid diagnostics, and environmental pollution monitoring as also in new drug discovery.\nThe technical goals of this project will (a) demonstrate the principle of slow light enhanced interferometry\non-chip; (b) investigate novel thin-film electro-optic phase shifters on silicon chip; (c) demonstrate on-chip\nreal time dual polarization interferometry; and (d) demonstrate an unprecedented fabrication tolerant silicon\nnanophotonic sensor operating in a compact package with electrical drive and electrical readout. The\nprogram will expose students to interdisciplinary research encompassing lithography, photonics, electrical\nengineering, physics, biochemistry, and materials science. The project will culminate with the development\nof a USB-powered handheld optical biosensor kit. Project members will engage in science and technology\noutreach targeting middle and high school students in greater Dayton, OH and greater Denton, TX counties.\nProject activities will outreach to broaden the participation of minority students in STEM education and\ntraining. Students will be exposed to an innovation ecosystem with hands-on science and technology\nexperience. Finally, the project will help to address the significant current need to build US-based\nmanpower in the design and manufacturing of semiconductor chips.\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": "10017", "attributes": { "award_id": "2228202", "title": "Pathway to STEM Success: Improving Access and Success and Closing Equity Gaps in College-level Math in a State Community College System", "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": 2077, "first_name": "Connie", "last_name": "Della-Piana", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-01-01", "end_date": "2026-12-31", "award_amount": 1969430, "principal_investigator": { "id": 3654, "first_name": "Di", "last_name": "Xu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25843, "first_name": "Catherine L", "last_name": "Finnegan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25844, "first_name": "Florence X", "last_name": "Ran", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25845, "first_name": "Laura", "last_name": "Desimone", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25846, "first_name": "Zachary", "last_name": "Beamer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 177, "ror": "", "name": "University of California-Irvine", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by increasing the success rates of community college students in gateway mathematics courses and contributing to the knowledge base on effective practices which support that success. Recognizing the limitations of current developmental education approaches to fostering student success in mathematics, the research and practice partnership (RPP) team of researchers and practitioners from the University of California – Irvine, the Virginia Community College System (VCCS), and the University of Delaware plan to investigate a VCCS initiative designed to reform the support to students who come from high schools that did not prepare them adequately for college. While current reforms focus on one element of developmental education, the RPP effort under study is implementing a more comprehensive approach to support these students as they progress through gateway mathematics. The VCCS initiative includes three components, identifying students who need support, providing concurrent academic support through co-requisite coursework, and providing proactive advising and coaching.\n \nThree studies will examine the effects of this effort: (1) a Reform Implementation Study to document how the reform is implemented at each institution and challenges encountered; (2) a Reform Impact Study to examine the impact of the reform on student gateway math outcomes, as well as downstream STEM pathway success and distal outcomes including degree attainment and labor market performance; and (3) a Math Instruction Study to explore whether specific ways of delivering the co-requisite support and teaching approaches are associated with better student outcomes and smaller racial gaps in gateway mathematics. The RPP also plans to examine how colleges responded to challenges presented by the ongoing COVID pandemic when implementing the reform, whether these changes will persist into the foreseeable future, and what teaching practices are effective in promoting math success by course delivery format. The mixed methods research design is well aligned with project goals, research methodologies, and analyses. The RPP team will collect data through surveys, targeted interviews, and administrative data ans plans to apply a comprehensive developmental education framework to analyze and interpret the data. The results will be used to inform the Direct Enrollment Reform at VCCS and should generate knowledge that other institutions may use to inform their efforts to improve student success, particularly when entry into and completion of gateway mathematics courses pose challenges. The NSF program description on Advancing Innovation and Impact in Undergraduate STEM Education at Two-year Institutions of Higher Education supports projects that advance STEM education initiatives at two-year colleges. The program description promotes innovative and evidence-based practices in undergraduate STEM education at two-year colleges. This project is also supported b the NSF IUSE-HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM.\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": "10018", "attributes": { "award_id": "2234206", "title": "Increasing Underrepresented Minorities in STEM: The 49th NOBCChE Annual Conference National Organization for the Professional Advancement of Black Chemists and Chemical Engineers", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "PROJECTS" ], "program_reference_codes": [], "program_officials": [ { "id": 1087, "first_name": "Rebecca", "last_name": "Peebles", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2023-08-31", "award_amount": 97673, "principal_investigator": { "id": 11948, "first_name": "Tyrslai", "last_name": "Williams", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 8291, "first_name": "Rena A", "last_name": "Robinson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 360, "ror": "https://ror.org/05ect4e57", "name": "Louisiana State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award will support the participation of undergraduate students, graduate students, and postdoctoral fellows at the national annual meeting for the National Organization for the Professional Advancement of Black Chemists and Chemical Engineers (NOBCChE) conference in 2022. This award is funded by Division of Chemistry. The NOBCChE meeting has become an important conference to recruit diverse global leaders in the Science, Technology, Engineering and Mathematics (STEM) fields and to create an inclusive environment in the broader scientific community. This conference will be held in person in 2022, for the first time since the COVID pandemic began. The PIs will use this as an opportunity to compare student outcomes between live and virtual conferences, providing insight that would be impactful to many other scientists and educators in STEM fields.\n\nThis award will provide opportunities for participants to engage in four major areas of focus: (1) STEM Professional and Technical Development, (2) STEM Mentoring and Networking Opportunities, (3) Exposure to Prestigious Minority Models in STEM, and Academic and Professional STEM Career Advancement Opportunities, and (4) access to potential careers in the STEM workforce. Student participants will interact with peers and professionals from industry, government, and academia to develop collaborations and to explore opportunities to pursue graduate degrees in the chemistry, chemical engineering and related disciplines. NOBCChE 2022 will also serve to enrich the technical, professional, and leadership skill sets of student attendees and students working on committees with other senior leaders on conference planning and execution.\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": "10019", "attributes": { "award_id": "2217460", "title": "Advancing Methods to Trace and Contextualize Space-Time Interaction Patterns in Movement Data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Methodology, Measuremt & Stats" ], "program_reference_codes": [], "program_officials": [ { "id": 3692, "first_name": "Cheryl", "last_name": "Eavey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2025-09-30", "award_amount": 229996, "principal_investigator": { "id": 2276, "first_name": "Somayeh", "last_name": "Dodge", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This research project will advance computational approaches to trace and characterize interactions and critical encounters between agents in a mobile network. Examples of networks include people in a city, a group of animals in an ecosystem, or a fleet of vessels. Despite the advances in tracking technologies, computational movement analysis methods remain limited in quantification and characterization of dynamic interaction patterns in large mobile networks. As the decade turned to the 2020s, society witnessed the widespread transmission of SARS-CoV-2 through respiratory droplets via close contacts and or lagged interactions between individuals. This led to a set of unprecedented non-pharmaceutical interventions including digital contact tracing to mitigate the spread of the COVID-19. However, current techniques are inefficient for tracing and detecting critical or risky encounters or temporally lagged interactions between healthy and potentially infected individuals. Using movement observations, this project will provide data-driven results about interactions between moving agents. The results will enhance contact-tracing technologies for examining potential human exposure to health risks or infectious agents. More generally, the methods to be developed will enable scientists to model social behaviors in human and animal networks. The project will create open-access/open-source analytical tools which will make spatial data science more accessible to researchers, educators, and students in geography and other fields. The project will provide training and research experiences for graduate students.\n\nThis research will develop and evaluate novel context-aware time-geographic analytical methods through optimized computational algorithms to (1) trace dynamic interactions and measure the duration and frequency of encounters between individuals using large movement data sets, and (2) to contextualize encounters, concurrent interactions, and lagged interactions to better identify critical or risky contacts. The research will investigate three overarching research questions: (1) How can we best leverage statistical approaches and time geographic methods for better estimation of contact through movement? (2) Given large movement observations, how can we effectively and efficiently trace and identify 'risky' or 'interesting' encounters between individuals? (3) Can interaction analytics be used to understand collective movement patterns in social networks of humans and animals? A set of case studies and open analytical tools will be developed to demonstrate the efficacy of the analytical framework using real GPS observations of people and animals. The analytical methods to be developed in this study will be generalizable to understanding interaction in both social and ecological systems, contributing new knowledge about social behavior of humans and competition of keystone species.\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": "10020", "attributes": { "award_id": "2210707", "title": "Collaborative Research: PIC: Slow Wave Enhanced Electrooptically Tuned Michelson Interferometer Biosensor for On-Chip Dual Polarization Interferometry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 763, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2025-08-31", "award_amount": 338076, "principal_investigator": { "id": 25847, "first_name": "Swapnajit", "last_name": "Chakravarty", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 214, "ror": "https://ror.org/021v3qy27", "name": "University of Dayton", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID pandemic of 2020 demonstrated the worldwide need for low-cost, highly sensitive, rapid\ndiagnostic testing of diverse pathogens. While silicon photonics enables such a highly multiplexed labelfree\nsensing capability with extremely high sensitivities, a handheld low-cost silicon nanophotonic sensor\nis still missing. Fabrication imperfections have made photonic sensor implementations difficult with a fixed\nwavelength laser and a single detector. Photonic measurement variabilities also arise from binding\nuncertainties in nanophotonic pillars and trenches. The fundamental work in this proposal employs a novel\non-chip dual polarization interferometry technique that will reduce photonic measurement variability, and\nnovel circuit implementations to enable electrically driven and electrically readout low-cost on-chip\nnanophotonic sensors. The working principle of the device, and circuit implementations of the device to\novercome fabrication and measurement limitations have not been previously demonstrated. The state-ofthe-\nart photonic device fabrication capabilities at a 300 mm CMOS foundry, namely AIM Photonics, with\nmonolithically integrated passive and active electrically biased photonic components will be employed in\nthis project. The project will involve students in optics, engineering, materials science, and physics from\nthe University of Dayton and the University of North Texas who will not only learn about cutting-edge\nSTEM (science, technology, engineering, and mathematics) research but also in computer aided design\nlayouts for foundry fabrication of next-generation co-integrated electronic-photonic devices. The project\nwill also work with students and faculty in microbiology from the Dayton Early College Academy, and\nother middle and high school students in the greater Dayton, OH and Denton, TX areas. The handheld\nsensors will find applications in various domains of biological sensing for cancer diagnostics, infectious\ndisease and opioid diagnostics, and environmental pollution monitoring as also in new drug discovery.\nThe technical goals of this project will (a) demonstrate the principle of slow light enhanced interferometry\non-chip; (b) investigate novel thin-film electro-optic phase shifters on silicon chip; (c) demonstrate on-chip\nreal time dual polarization interferometry; and (d) demonstrate an unprecedented fabrication tolerant silicon\nnanophotonic sensor operating in a compact package with electrical drive and electrical readout. The\nprogram will expose students to interdisciplinary research encompassing lithography, photonics, electrical\nengineering, physics, biochemistry, and materials science. The project will culminate with the development\nof a USB-powered handheld optical biosensor kit. Project members will engage in science and technology\noutreach targeting middle and high school students in greater Dayton, OH and greater Denton, TX counties.\nProject activities will outreach to broaden the participation of minority students in STEM education and\ntraining. Students will be exposed to an innovation ecosystem with hands-on science and technology\nexperience. Finally, the project will help to address the significant current need to build US-based\nmanpower in the design and manufacturing of semiconductor chips.\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": "10021", "attributes": { "award_id": "2228610", "title": "CIVIC-PG Track B: Economic Resiliency through Mechanism Design and Secure Computing: MainStreetPulse: An Early Warning Platform for Monitoring and Supporting Main Street Businesses", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 25848, "first_name": "Kimberly", "last_name": "Zarecor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2023-03-31", "award_amount": 50000, "principal_investigator": { "id": 25852, "first_name": "Kira", "last_name": "Goldner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25849, "first_name": "Mayank", "last_name": "Varia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25850, "first_name": "Ziba P", "last_name": "Cranmer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25851, "first_name": "Langdon D", "last_name": "White", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 168, "ror": "", "name": "Trustees of Boston University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The project seeks to better understand the health of individual small businesses and Main Street business districts in Boston using cryptography, incentives, and publicly available data. Using these techniques, the city can improve planning recommendations for Main Street Districts and proactively identify distressed businesses for targeted interventions and support. The project ultimately aims to create replicable tools that can be used by all cities to protect and grow the small business sector which serves as the backbone of the American economy, accounting for 44% of all economic activity and 47% of all U.S. employees, according to the U.S. Bureau of Labor Statistics. It will also provide tools to prevent and mitigate the impact of business closures on Main Street districts. This challenge became even more acute during the first months of the SARS-CoV-2 pandemic when an estimated 100,000 businesses were forced to close, a volume much higher than the approximately 600,000 business that exit each year.\n\nTo achieve this, the research team will develop a predictive model and a user-friendly application using mechanism design and secure computation. The envisioned cloud-native tool can be used by city planners and by individual businesses to make critical decisions such as location citing, rent and payroll budgets, and sales forecasts that leverage projected population shifts. The research questions for Stage 1 are primarily centered around the feasibility of designing a mechanism to predict the distress or closure of small businesses, based on the application of existing data sources along with sensitive data contributed by business owners as a result of the application of mechanism design and secure computation techniques. This model will be tested at the neighborhood scale in the Stage 2 pilot. Participants will be incentivized to contribute business data, because secure multi-party computation will allow the team to develop the model without ever having access to the original data.\n\nThis project is in response to the Civic Innovation Challenge program—Track B. Bridging the gap between essential resources and services & community needs—and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy.\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": "10022", "attributes": { "award_id": "2225755", "title": "RII-BEC: Enhancing the Transition of COVID-19 Disadvantaged Students from Undergraduate to Graduate Studies in STEM through Multi-Year Undergraduate Research Experiences", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director", "EPSCoR Research Infrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 15644, "first_name": "John", "last_name": "Haddock", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2027-08-31", "award_amount": 999595, "principal_investigator": { "id": 25856, "first_name": "Levent", "last_name": "Atici", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25853, "first_name": "Eduardo", "last_name": "Robleto", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25854, "first_name": "Kurt M", "last_name": "Regner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25855, "first_name": "Sarah", "last_name": "Harris", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 247, "ror": "", "name": "University of Nevada Las Vegas", "address": "", "city": "", "state": "NV", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). \n\nThis project at the University of Nevada Las Vegas (UNLV) aims to serve the national interest by increasing the number of students completing undergraduate and graduate degrees and pursuing careers in science, technology, engineering, and mathematics (STEM) fields, which is critical to United States global competitiveness. The project is designed to mitigate challenges and barriers imposed by the COVID-19 pandemic on students who have been the most affected in STEM disciplines, namely students from groups who have been historically underrepresented in STEM. The investigators will engage a cohort of twenty students who will be guided for four years from undergraduate studies to graduate school. The project will provide these students with onboarding, academic preparation, advising and educational planning, social and networking opportunities, and financial support to increase student retention, progression, and graduation rates. The project aims to use these interventions to improve the students' preparation as undergraduates for transition to graduate school and STEM careers. In this direction, the project will introduce a structured undergraduate research program that provides academic preparation and authentic mentored research engagement that spans across four years of study. The project will highlight the scientific process and its significance, and will study the project's effectiveness on building students' STEM identity and sense of belonging. Project activities will help lead to a more diverse and competitive STEM workforce in Nevada.\n\nThe underlying goal of the project is to employ best practices coupled with creative approaches to provide students with a knowledge base and training for entering and succeeding in graduate school. To support this goal, additional goals include providing students with: (1) an improved perspective and sense of belonging for students in STEM fields; (2) an understanding of, and experience with, research and the scientific process; and (3) opportunities to participate in mentored research experiences. The project will achieve these goals through sustainable intra-partnership collaborations among UNLV’s Office of Undergraduate Research, College of Engineering, College of Sciences, Graduate College, advising community, and STEM departments and faculty to provide mentoring, academic support and social support, professional opportunities, and funding. Each student participant will receive individualized as well as cohort support, and the project will unfold in four phases: Year/Phase 1 - retention, placement, advising, mentoring, and STEM identity formation; Year/Phase 2 - research readiness and self-efficacy building; Year/Phase 3 - mentored authentic research experiences; and Year/Phase 4 - graduate school readiness. The project’s scope is to investigate and advance understanding of the impacts of the global pandemic on student success and transition to graduate school in a STEM discipline. Through mixed-methods evaluation, the project will develop and disseminate a model on how to mitigate the disproportionate impact of COVID-19 on undergraduate students from underrepresented groups, while improving students' STEM identity and self-efficacy and preparing them for graduate school and, later, for the STEM workforce. The award is co-funded by the Louis Stokes Alliances for Minority Participation program.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10023", "attributes": { "award_id": "2222907", "title": "EAGER: PAN-VARIANT COVID-19 DIFFERENTIATED BIOSENSING USING GRAPHENE FIELD-EFFECT SENSORS", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 763, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2024-02-29", "award_amount": 200000, "principal_investigator": { "id": 4035, "first_name": "Deji", "last_name": "Akinwande", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "EAGER: PAN-VARIANT COVID-19 DIFFERENTIATED BIOSENSING USING GRAPHENE FIELD-EFFECT SENSORS\n\n\n\nNontechnical:\nThe pandemic due to COVID-19 has triggered an increasing demand for scientific research into portable fast biosensors capable of rapid infectious disease detection. With the rise of variants beyond the original COVID-19 strain, there is a need for the scientific understanding and development of biosensors that can be rapidly adapted to sense existing, emerging and new variants of the virus. This award will investigate facile, precise, multiplexed biosensors for detection of the virus from the different evolving variants based on advanced nanomaterials, namely, functionalized graphene field-effect transistors that has the potential for unprecedented sensitivity and a fast response time in a low-cost platform.\n\n\nTechnical:\nThis research aims to pioneer a single platform for rapid multi-variant detection based on functionalized graphene-antibody selective interaction that affords a high intrinsic limit of detection. Different variants of Covid-19 will be evaluated with the antibody-functionalized graphene field-effect sensing platform. The outcomes of this research will generate new knowledge on multi-variant biosensing, limits of detection, and cross-reactivity through structural and conceptual engineering. The sensor platform is sufficiently versatile to be adapted to detect both existing, emerging, and future variants.\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": 1385, "pages": 1405, "count": 14046 } } }