Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=-end_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-end_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-end_date", "next": null, "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=-end_date" }, "data": [ { "type": "Grant", "id": "15663", "attributes": { "award_id": "2449747", "title": "I-Corps: Translation Potential of Safe Biomedical Perfusion Technologies", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-12-01", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 3094, "first_name": "Hosam", "last_name": "Fathy", "orcid": "https://orcid.org/0000-0002-4714-2466", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this I-Corps project is the development of a safety control system for biomedical perfusion applications. Perfusion involves the circulation of an external fluid through a body compartment, often using surgically inserted catheters. Currently, multiple safety risks exist during perfusion procedures, including the risk of blood flow occlusion during high-pressure perfusion as well as the risk of tissue damage resulting from high perfusion and/or drainage flowrates. Many existing and emerging biomedical applications involve the use of perfusion, especially through the abdominal cavity. These applications include peritoneal dialysis, hyperthermic intraperitoneal chemotherapy (HIPEC), and peritoneal oxygenation. This technology is designed to address the safety risks, which may have the potential to improve the safety of biomedical perfusion and potentially improve patients’ outcomes. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a control algorithm for the safe perfusion of oxygenated perfluorocarbons through the peritoneal cavity. Perfusion plays a critical role in both existing life-saving biomedical interventions (such as peritoneal dialysis, hyperthermic intra-peritoneal chemotherapy, and extra-corporeal membrane oxygenation) as well as emerging interventions (such as peritoneal oxygenation). This technology utilizes perfusion flowrate, pressure, and volume sensors to estimate and manage multiple perfusion safety hazards simultaneously. These technologies were developed as an emerging medical intervention where oxygen-rich liquids are perfused through patients’ abdomens to enable oxygen transport into the bloodstream via diffusion. This technology has the potential to allow the abdomen to serve as a “third lung”, particularly for patients with respiratory failure due to ailments such as COVID-19. The solution will improve the safety during peritoneal oxygenation and more broadly during any biomedical perfusion application. 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": "15664", "attributes": { "award_id": "2438012", "title": "I-Corps: Translation Potential of an Enhanced Fluorescence-based Diagnostic Technology for the Detection of Lyme Disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 31316, "first_name": "Jaime A.", "last_name": "Camelio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-12-01", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32172, "first_name": "Nathaniel", "last_name": "Cady", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 571, "ror": "", "name": "SUNY at Albany", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this I-Corps project is the development of an enhanced fluorescence-based diagnostic technology for the detection of antibodies and other biomarkers for disease diagnostics. The base technology has been demonstrated for diagnosing high profile diseases including COVID-19 and Lyme disease. For this I-Corps effort, Lyme disease has been chosen as the beachhead market due to the current diagnostic challenges, and the growing market for fast and accurate Lyme disease diagnostic technologies. The accepted standard for Lyme disease, known as standard two-tiered testing (STTT) is time consuming, requires specialists to run, and can be unreliable, especially for early stages of the disease. This technology has proven to alleviate these pain points, providing rapid and accurate Lyme disease diagnosis, especially for early Lyme disease patients. The platform has also been utilized for detecting RNA-protein and DNA-protein interactions, which potentially broadens its utility for a large number of different disease diagnostic applications, biomarker discovery, and biological / pharmaceutical research applications. The technology may have impact in several clinical and biological research fields. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a photonics-based Lyme disease diagnostic platform. Lyme disease is the most common vector-borne disease in the United States and, despite advances, remains a considerable diagnostic challenge. Confirmatory diagnosis requires a second test, performed in series, often in batches, and at a centralized laboratory. This delay can lead to considerable morbidity in disseminated Lyme disease. This technology is a low-cost, highly sensitive, fluorescence-based platform, which provides a rapid, easy-to-use, and highly accurate Lyme test that could be used outside traditional clinical laboratories or for more rapid and accurate diagnosis within clinical laboratories. The proof-of-principle research positions the technology as a rapid (<40 minutes total test time) and reliable alternative to traditional Lyme tests, while retaining the full sophistication of a two-tiered testing system. 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": "15665", "attributes": { "award_id": "2444914", "title": "I-Corps: Translation Potential of an Online Healthcare Information (OHI) Trust Badge", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-11-15", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32174, "first_name": "Ankur", "last_name": "Chattopadhyay", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32173, "first_name": "Seth A", "last_name": "Adjei", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1396, "ror": "https://ror.org/01k44g025", "name": "Northern Kentucky University", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this I-Corps project is the development of an online knowledge recommender tool and trust badge for consumers. Health misinformation remains a serious societal threat. Since the emergence of the COVID-19 pandemic, reports show on average that 8 out of 10 Americans search for online healthcare information (OHI), and 4 out of 10 Americans cannot correctly identify false healthcare claims. The goal of the new technology is to help alleviate confusion amongst consumers caused by the overwhelming amount of OHI, and to help OHI providers boost their reputation as a trustworthy source. The tool is designed to combat misinformation by proactively serving a wide spectrum of stakeholders who regularly deal with OHI content. The I-Corps project will focus on the specific issues and public challenges of endorsements in addition to fact checking of OHI content and contributing to a better understanding of the needs of people who use and/or provide OHI content. This solution serves as a foundation for a consultancy service providing platform offering advice plus training to OHI consumers and OHI providers. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a software tool that will serve online healthcare information (OHI) users by providing machine learning-based classification and certification of OHI content trustworthiness. Research has shown that machine learning-based classifiers can process OHI claims and classify them as fact or fake, but such solutions have not been directly integrated into web browsers and have been trained with primarily textual cues from mostly unimodal datasets. This technology addresses these limitations and is designed as a machine learning driven online knowledge recommender tool, prototyped as a web extension utility, which can be directly embedded into web browsers to seamlessly report trustworthiness of any OHI content. the solution is designed as a trust badge model for easy certification of web content and can function both as an online content classifier. This capability may allow both OHI consumers and OHI providers to validate and tag OHI websites' trustworthiness. Additionally, the solution is trained with multimodal data, that includes both textual and visual cues (e.g., image elements, graphic contents, and infographics), unlike existing solutions that do not include visual cues or image artifacts. 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": "15666", "attributes": { "award_id": "2417241", "title": "SBE-UKRI: Resource Rational Contractualism: A foundation for moral judgment and decision making", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Decision, Risk & Mgmt Sci" ], "program_reference_codes": [], "program_officials": [ { "id": 652, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-15", "end_date": null, "award_amount": 690003, "principal_investigator": { "id": 32175, "first_name": "Fiery", "last_name": "Cushman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 455, "ror": "https://ror.org/03vek6s52", "name": "Harvard University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Often people need to make moral decisions in cases where no clear rules apply. For instance, during the COVID pandemic people often faced difficult tradeoffs and choices in situations where no clear rules existed. Philosophers have suggested that one promising approach is for a person to act in the way that they believe everybody would agree to, if there were time for everybody to talk things over. This research project asks whether ordinary people imagine a kind of bargain between interested parties and regard the imagined outcome of the bargain as the most morally appropriate solution. The project develops precise computational models of how people do this, and then tests the models by conducting experiments. These experiments ask people to reason about everyday situations, and also puts people in structured economic exchanges with each other to explore whether their choices reflect basic principles of bargaining. Finally, the project embeds bargaining principles within current artificial intelligence (AI) 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": "15667", "attributes": { "award_id": "2514354", "title": "Collaborative Research: RESEARCH-PGR: Extracellular RNA Produced By Plants: What, Where, How, Who, and Why?", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Plant-Biotic Interactions" ], "program_reference_codes": [], "program_officials": [ { "id": 954, "first_name": "Gerald", "last_name": "Schoenknecht", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 1200000, "principal_investigator": { "id": 32176, "first_name": "Blake", "last_name": "Meyers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 955, "first_name": "Patricia", "last_name": "Baldrich", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 276, "ror": "", "name": "University of California-Davis", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project investigates the role of secreted RNA in the immune system of plants. The Innes and Meyers laboratories recently discovered that the leaves of plants accumulate RNA in the spaces between cells and on their surfaces. Although we usually think of RNA as a molecule that can direct cells to synthesize specific proteins (e.g., the mRNA in COVID vaccines directs our cells to make SARS-CoV2 spike protein), some RNAs serve other functions. Analysis of the base sequences of plant extracellular RNAs revealed that these RNAs are diverse in sequence, but do not appear to encode proteins. The discovery of extracellular non-coding RNA in plants raises two fundamental questions that this project will address: 1) how do plants secrete RNA? and 2) what is the function of this RNA? It takes a large amount of energy for cells to secrete RNA, thus this secreted RNA must benefit the plant in some way. This project will test the hypothesis that secreted RNA functions to protect plants from infection by fungi and bacteria. If this hypothesis is correct, the proposed research will enable generation of crop plants with improved immune systems that are more resistant to disease. Such crops are needed to feed a growing global population in a sustainable manner, while reducing the environmental impacts of agriculture. The Innes and Meyers laboratories recently discovered that the apoplast of Arabidopsis leaves contains abundant long non-coding RNAs, including circular RNAs, as well as small RNAs. These RNAs are bound to protein particles, which protects them against degradation. Notably, this extracellular RNA (exRNA) is highly enriched in the post-transcriptional modification N6-methlyadenine (m6A). These discoveries raise fundamental questions about plant biology: Are there specific exRNAs that are broadly conserved across plant species? How are exRNAs secreted, and are post-transcriptional modifications central to this process? And why do plants produce exRNAs? Do they play a fundamental role in plant-microbe interactions? To answer these questions, exRNA will be purified from the apoplast and leaf surfaces of seven diverse species: Arabidopsis, soybean, tomato, lettuce, pineapple, rice, and maize, which were chosen based on their phylogenetic diversity, genomic resources, importance as crops, and diversity in physiology. These exRNAs will be analyzed using both RNA-seq and sRNA-seq, which will allow identification of RNAs that are conserved between species. To assess whether m6A or other modifications are required for secretion, transgenic plants that express exRNAs that lack modification sites will be tested for their secretion efficiency. To investigate additional requirements for exRNA secretion, the exRNA content in Arabidopsis and rice plants with mutations in known RNA binding proteins and secretory pathway genes will be analyzed. Lastly, to assess whether exRNAs contribute to immunity, mutants compromised in exRNA secretion will be tested for resistance to fungal and bacterial pathogens. This award was co-funded by the Plant Genome Research Program and the Plant Biotic Interactions Program in the Division of Integrative Organismal Systems. 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": "15668", "attributes": { "award_id": "2525493", "title": "PIPP Phase I: Develop and Evaluate Computational Frameworks to Predict and Prevent Future Coronavirus Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 2558, "first_name": "Joanna", "last_name": "Shisler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 1000000, "principal_investigator": { "id": 8018, "first_name": "Hong", "last_name": "Qin", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 894, "ror": "", "name": "University of Tennessee Chattanooga", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 237, "ror": "", "name": "Old Dominion University Research Foundation", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Once a novel coronavirus or a new variant is detected, how likely would the novel coronavirus or new variant transmit from person to person, and how sick could patients become? What kind of new coronaviruses could cause future pandemics? Knowing the answers to these questions can help nations make proper strategic decisions. The dilemma is how to predict the behavior and pathogenic severity of new viruses as early as possible. A team of researchers thinks they have found ways to answer these questions by developing new artificial intelligence software tools to predict the virus’s behaviors based on its genome sequence. This team of researchers recognizes the potential bias in machine learning applications and the need to increase diversity in the future artificial intelligence workforce. Leveraging their expertise in genomics, data science, artificial intelligence, genetics, infectious disease, chemical engineering, public health, and communication, this team of researchers will organize training workshops and activities providing culturally responsive teaching of artificial intelligence, data science training to teachers, and context-relevant coding experiences to high school students. The team will promote public trust in science and discernment of misinformation through community outreach. This research team will prototype a deep learning model based on biological knowledge and hypotheses that can predict viral pathogenic fitness from genomic sequences to test the potential rules for viral pathogenicity. The team will explore several methods to correct the sampling bias in viral genomic surveillance in order to accurately estimate the fitness of a viral strain. The team will investigate the mutation and recombination profiles in all available bat coronavirus genomes from the Southeastern Asia and build a prototype geospatial model to predict the recombination probability for all available bat coronaviruses. Leveraging their expertise in genetics and macromolecular structure modeling, the team will test a few candidate genes in SARS-CoV-2 for potential pathogenic rules. Based on the outcomes of these pilot projects, the team will be able to estimate the pathogenic fitness of an emerging SARS-CoV-2 variant or another novel coronavirus. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG) and Social, Behavioral and Economic Sciences (SBE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15669", "attributes": { "award_id": "2505723", "title": "Preparing for the Future of the STEM Teacher Workforce in the 21st Century: Leveraging Multi-contextual Evidence", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "ECR-EDU Core Research" ], "program_reference_codes": [], "program_officials": [ { "id": 8210, "first_name": "Andrea", "last_name": "Nixon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 201972, "principal_investigator": { "id": 6136, "first_name": "James", "last_name": "Anglum", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 734, "ror": "https://ror.org/01p7jjy08", "name": "Saint Louis University", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 341, "ror": "https://ror.org/012afjb06", "name": "Lehigh University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to identify trends in the demographics and turnover behavior of the STEM teacher workforce. It focuses on specific remedies and investments needed to retain prospective teachers in high-need schools, especially rural communities, and to improve outcomes for their students. The project includes two complementary studies, one using the National Teacher and Principal Survey (an update from the earlier Schools and Staffing Survey) and a second, using longitudinal administrative data from the states of Kansas and Missouri. The project will explore a range of questions, including: how STEM teacher demographics, turnover intentions, and actual turnover may have changed nationally due to the COVID-19 pandemic; and how the Great Recession of 2007-08 and the COVID-19 pandemic may have influenced teacher outcomes. This body of research will produce insights to inform actionable recruitment and retention practices for high-need school districts and future research focused on teacher labor markets. The conceptual framework and proposed analyses build upon a model of teacher turnover that suggests three main categories of factors that drive teacher turnover: teacher factors, school factors, and external factors and events. Leveraging this framework, the investigators group the research questions and analyses into three broad themes: STEM teacher characteristics, the school and student characteristics in which STEM teachers are employed, and contemporary secular trends that impact STEM teachers. They also consider the interplay between the STEM teacher characteristics and the school context in which STEM teachers work. The investigating team will employ descriptive and regression analyses to answer the research questions. Across interconnected lines of inquiry, the researchers will balance national generalizability with comprehensive state-specific application to inform current and future practice, policy, and research. This project is supported by the EHR Core Research(ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent issues in STEM education. 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": "15670", "attributes": { "award_id": "2519581", "title": "EAGER: Private Blockchain-Enabled Federated Learning Framework for Distributed Manufacturing Networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "MSI-Manufacturing Systms Integ" ], "program_reference_codes": [], "program_officials": [ { "id": 31283, "first_name": "Janis", "last_name": "Terpenny", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 299999, "principal_investigator": { "id": 31284, "first_name": "Thorsten", "last_name": "Wuest", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 406, "ror": "", "name": "University of South Carolina at Columbia", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true }, "abstract": "In recent years, global manufacturing networks experienced a variety of shocks and disturbances including COVID-19. Thus, improving network resiliency, transparency, and cybersecurity have emerged as a national priority. Smart Manufacturing technologies such as Artificial Intelligence and Machine Learning show promise in achieving these objectives, yet struggle to materialize at the manufacturing network level. Particularly small and medium-sized manufacturers struggle in their adoption of these data-driven, value added technologies due to a lack of resources and incentives. Consequently, they cannot participate in many high-value manufacturing networks that often require certain technologies and data sharing. This EArly-concept Grant for Exploratory Research (EAGER) project supports research that intends to address this challenge through a Blockchain-enabled framework that leverages secure and private Federated Learning which meets the unique requirements of defense manufacturing networks. This framework enhances the availability and integrity of critical supplies, as well as strengthens and diversifies the defense industrial base. The project’s secure and privacy-preserving data sharing and collaboration mechanisms can be applied in various domains beyond manufacturing, such as healthcare, finance, and supply chain, empowering individuals and organizations to share data securely and collaborate effectively. The results have potential to transform industry, drive economic growth, foster innovation, and enhance societal well-being. The project’s research problem stems from manufacturing networks’ inability to securely and efficiently exchange data and leverage network level Federated Learning. The project aims to increase the resiliency of distributed and dynamic manufacturing networks, specifically including small and medium-sized manufacturers, by providing access to a secure private Blockchain platform that enables decentralized, secure, and transparent communication channels. This enables manufacturing network level learning through Federated Learning while respecting data ownership and ensuring retention of competitive or controlled (raw) data and machine learning models. To achieve these goals, the project utilizes Federated Learning by integrating a private Blockchain to manage metadata, access controls, and model updates. Unlike existing approaches, the framework focuses on specific challenges and requirements of manufacturing networks. This means ensuring confidential data remains local under full control of the individual nodes while leveraging Blockchain for efficient coordination of the Federated Learning process as well as reducing overhead cost for smaller network participants that are resource constraint. The project advances the state-of-the-art in Federated Learning and Blockchain technology through efficient algorithms for model aggregation and coordination in the presence of heterogeneous data for manufacturing networks. 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": "15671", "attributes": { "award_id": "2517733", "title": "Collaborative Research: RI: Medium: Transparent Fair Division of Indivisible Items", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [ { "id": 32177, "first_name": "Andy", "last_name": "Duan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 621874, "principal_investigator": { "id": 2616, "first_name": "Lirong", "last_name": "Xia", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 148, "ror": "https://ror.org/01rtyzb94", "name": "Rensselaer Polytechnic Institute", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Fair division deals with the distribution of resources and tasks among different parties, e.g., individuals, firms, nations, or autonomous agents, with the goal of achieving fairness and economic efficiency. Fairness has increasingly become crucial in distributing precious and scarce medical equipment, and its absence has exacerbated healthcare issues during the COVID-19 global pandemic. A wide variety of real-world applications such as scheduling, dispute resolution, healthcare management, and refugee settlement assume complete knowledge about allocation decisions, which gives rise to negative computational and impossibility results. The existing approaches to mitigate these challenges, in turn, impose a high cost on transparency. The broad goal of this project is to provide theoretical and algorithmic solutions for fair allocation of indivisible items in practical, large-scale settings, as a broad contribution to the grand scheme of artificial intelligence (AI) and economics for social good. This research will offer a novel and promising perspective for developing practical and transparent fair solutions while providing a systematic investigation on the perceived fairness of allocation mechanisms that are applicable to societies at large. This project will integrate and develop algorithmic solutions for transparent fair division in a publicly available software system with the goal of extending its reach--and in general promoting fairness and transparency--to a broad national and international audience. This project will develop a new framework for achieving fairness and efficiency in the allocation of indivisible resources with minimum cost on transparency. Specifically, it will make progress in four interconnected dimensions: 1) Tradeoffs between transparency, fairness, and efficiency, that aim at analyzing the compatibility of the properties and devising algorithmic solutions when allocating indivisible items, 2) Strategic aspects of fair division, that investigates agents' behavior and strategies under transparency requirements, 3) Domain restriction, that focuses on developing tractable solutions by circumventing the impossibility results in achieving compatible solutions, and 4) Bads and mixtures, that extend the transparency and fairness framework to include desirable (goods) and undesirable items (bads). Furthermore, this research plans to close the current gap between theoretical foundations of fairness and the perception of fairness through a series of comprehensive empirical evaluations. 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": "15672", "attributes": { "award_id": "2515340", "title": "BII: Predicting the global host-virus network from molecular foundations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Cross-BIO Activities" ], "program_reference_codes": [], "program_officials": [ { "id": 2511, "first_name": "Daniel", "last_name": "Marenda", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 12456537, "principal_investigator": { "id": 25389, "first_name": "Colin", "last_name": "Carlson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "The Viral Emergence Research Initiative Biology Integration Institute (VERENA BII) will integrate data and biological theory across the fields of microbiology, immunology, ecology, evolution, and global change biology, working towards a unified understanding that improves our ability to predict viral emergence. The COVID-19 pandemic highlights a pressing need to understand the ecology and evolution of emerging viruses. These global dynamics are determined first and foremost by the genetic code of both viruses and their hosts, and by microscopic interactions between the two at the level of proteins and cells. However, biologists frequently struggle to connect theory across these scales. At the heart of this research effort is an open clearinghouse of big data, creating new opportunities to apply artificial intelligence to real-world problems. To foster a core set of data fluency and interdisciplinary research skills, the Lighthouse Learning Community will train participants at every career stage in the boundary-spanning science of the host-virus network, including more than 100 early career scientists. Undergraduates will be introduced to both biology and data science through a Course-based Undergraduate Research Experience in “The Fundamentals of Disease Surveillance,” while graduate students and postdoctoral fellows will explore these methods deeper through a biology integration workshop series, including a new Summer in the Capitol program in Washington, D.C. This cohort of emerging scholars will use open source materials, K-12 outreach, and digital media to harness public interest in emerging diseases like COVID-19, raising awareness about key issues while sharing the importance of basic biological research to save lives and protect ecosystems. To identify the mechanistic and molecular Rules of Life that govern host-virus dynamics at planetary scales, the VERENA BII will leverage a unique mix of data synthesis, computational innovation, field sampling, and laboratory experiments to identify the molecular underpinnings of host-virus interactions. An unprecedented comparative study of the chiropteran within-host environment will generate and test hypotheses about the immunological adaptations that allow bats to tolerate deadly viruses. In parallel, model-guided experiments will measure the features of the invertebrate immune system that play the greatest role in mosquitoes’ competence as arboviral vectors. Together, these model systems will illuminate the hard-coded basis of host-virus compatibility, supporting new machine learning methods to predict ecological and evolutionary networks and anticipate global risks of viral emergence in a changing climate. More broadly, the VERENA BII will expand an existing role as a hub of open data, software, and cyberinfrastructure for host-virus interactions, experimental virology, and wildlife disease surveillance. 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": 1392, "pages": 1392, "count": 13920 } } }