Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=-id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-id", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=-id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=-id" }, "data": [ { "type": "Grant", "id": "463", "attributes": { "award_id": "2204662", "title": "RAPID: Collaborative Research: Metapopulation Modeling to Develop Strategies to Reduce COVID-19 Transmission in Public Spaces", "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": 923, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2023-05-31", "award_amount": 25296, "principal_investigator": { "id": 924, "first_name": "Davida", "last_name": "Smyth", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 251, "ror": "", "name": "Texas A&M University-San Antonio", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 251, "ror": "", "name": "Texas A&M University-San Antonio", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic presents an unprecedented challenge to public and private institutions to safely reopen public spaces, including workspaces and schools. However, we have little guidance on how to manage the use of shared spaces in light of a highly transmissible, but invisible, pathogen. The fundamental aim of this project is to better understand how SARS-CoV-2 spreads in built environments. Predictions generated by mathematical modeling will be experimentally tested using a surrogate non-pathogenic virus. This project presents a new paradigm where the likelihood of infected individuals being present, the amount and manner of viral shedding, the locations of viruses over time, and the usage-needs of a location provide for a major advancement in the assessment of public space occupancy and usage. The ultimate goal is to develop practices capable of limiting virus transmission and meeting the current worldwide challenge to public health. Recommendations will resemble established building and fire codes, which regulate how space is allotted per occupant based upon design and usage requirements; our analyses will generate a “COVID Code” that can be generalized for use during future outbreaks. This research will also provide training opportunities for students and postdoctoral scholars. A recently developed computational model (the Ephemeral Island Metapopulation Model (EIMM)) that applies metapopulation theory to explain how pathogens persist in hospital environments will be revised to address the spatial spread of SARS-CoV-2 within built environments. The EIMM defines aspects of the built environment as distinct habitable zones of occupancy (“demes”) in much the same manner as human hosts are considered, but these demes have their own biological parameters relevant to the survival and transmission of SARS-CoV-2. The number and size of both living and non-living demes, instead of human hosts alone, are used to model size and location of pathogen populations using ecologically relevant parameters, such as growth rate, population size, and carrying capacity. An enveloped bacteriophage phi6 will be used to validate model expectations as well as test control strategies in real environments such as classrooms. The goal is to test which interventions suggested by the EIMM minimize opportunities for phage phi6 spread in shared spaces, and this information can be adapted to provide estimates of how various interventions would affect SARS-CoV-2 persistence and transmission.This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": "462", "attributes": { "award_id": "2136508", "title": "SBIR Phase I: Ace2 decoy as a pan-coronavirus therapeutic (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 921, "first_name": "Kaitlin", "last_name": "Bratlie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2022-12-31", "award_amount": 256000, "principal_investigator": { "id": 922, "first_name": "Gabriel Glenn A", "last_name": "Gregorio", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 250, "ror": "", "name": "WHITE ROCK THERAPEUTICS", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 250, "ror": "", "name": "WHITE ROCK THERAPEUTICS", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the advancement of a patient-friendly and cost-effective way to prevent and treat COVID-19 infection arising from SARS-CoV-2 and its variants. The inability to quickly stop the spread of respiratory infectious pathogens can have devastating global consequences, resulting in millions of deaths and creating an enormous economic burden. This project will prove the viability of an aerosolized pan-coronavirus neutralizing agent that can be delivered directly to the lungs, either as an early-stage, post-infection treatment or as a prophylactic. An inhalable therapeutic has a stronger commercial potential than the currently approved monoclonal antibodies which require intravenous delivery, and this drug will be more likely to retain potency against future variants. The COVID-19 virus is expected to persist in the human population, and novel variants thereof will continue to emerge. Therefore, this technology could be crucial in addressing these ongoing medical needs.This Small Business Innovation Research (SBIR) Phase I project aims to demonstrate in vivo efficacy of an inhalable decoy receptor that would effectively inhibit SARS-CoV-2 interaction with its endogenous cellular target and thus prevent infection of the host. The mechanism of SARS-CoV-2 viral entry into respiratory epithelial cells depends on the binding of viral Spike trimer to the host Ace2 receptor. The decoy receptor approach would use a recombinant soluble version of the Ace2 receptor that would bind and coat the viral particle, competing for Spike interaction with endogenous Ace2 and thus prevent virus docking to the cell surface. Stabilizing mutations in the Ace2 protein could enable it to act as a decoy receptor and also have sufficient stability in an inhalable formulation, allowing it to be deployed directly to the respiratory tract via a nebulizer. The dependence on Ace2 receptor binding is a potential Achilles heel of coronaviruses, as it is unlikely that SARS-CoV-2 or similar coronaviruses can mutate around the requirement to interact with this host protein.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": "461", "attributes": { "award_id": "2125600", "title": "SCC-IRG Track 1: Serving Households in AReas with food Insecurity with a Network for Good: SHARING", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 915, "first_name": "Michal", "last_name": "Ziv-El", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2025-09-30", "award_amount": 2018000, "principal_investigator": { "id": 920, "first_name": "Julie E", "last_name": "Ivy", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 916, "first_name": "Munindar P", "last_name": "Singh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 917, "first_name": "Lauren", "last_name": "Davis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 708, "ror": "", "name": "North Carolina Agricultural & Technical State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, { "id": 918, "first_name": "Leila", "last_name": "Hajibabai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 919, "first_name": "Irem Sengul", "last_name": "Orgut", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project seeks to address hunger relief in the US by maximizing equitable access to safe food, while considering the food preferences of food-insecure households, and simultaneously addressing redistribution of usable food that would otherwise be wasted. In the US, 10.5% of the households were food insecure in 2019, and that number increased by 29% in 2020 with the spread of COVID-19, according to the Feeding America Covid-19 impact assessment. Yet, it is estimated that 30–40% of food supply is wasted in the US. Working with two food banks in North Carolina and one in Alabama, each serving a range of counties—together with their associated networks of food-insecure households, food-secure households, other nonprofit organizations, and local businesses such as growers, supermarkets, restaurants, and other businesses in the service regions—the project team of academic and community partners will co-develop a community-based socially intelligent nonprofit food rescue and distribution infrastructure and platform to use community resources to equitably serve food-insecure households.This project unites three aims to develop a socially intelligent nonprofit food rescue and distribution infrastructure to equitably serve food-insecure households by continually learning their preferences with feedback to upstream stages of the supply chain. Aim 1, Smart Sociotechnical Information Capturer and Predictor: Understand the behavior of donors, beneficiaries, and volunteers by creating a socially intelligent infrastructure that records data in real-time and learns evolving stakeholders’ and end users’ needs, preferences, and utilization over time. Aim 2, Tactical Supply Chain Planner: Design and optimize the community food sharing network in response to stakeholder behaviors by constructing a technology and data-driven supply chain framework that adapts to evolving stakeholder behaviors to best serve the hunger needs of food-insecure households within the community. Aim 3, Real-Time, Logistics Operations Optimizer: Satisfy beneficiary needs through communal self-renewal by connecting food-insecure households to community-based supply options in real-time, and optimizing real-time pickup and delivery logistics while adhering to food safety time windows. The proposed infrastructure will facilitate more effective food distribution aimed at reducing hunger while simultaneously enhancing sustainability.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": "460", "attributes": { "award_id": "2111915", "title": "SBIR Phase II: Advanced Artificial Intelligence for Robotic E-Commerce Pick-and-Pack Automation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 913, "first_name": "Muralidharan", "last_name": "Nair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2024-01-31", "award_amount": 972586, "principal_investigator": { "id": 914, "first_name": "Jeffrey", "last_name": "Mahler", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 249, "ror": "", "name": "Ambi Robotics, Inc.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 249, "ror": "", "name": "Ambi Robotics, Inc.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve the resiliency of the supply chain by implementing flexible robotic systems for materials handling. The robotic systems that are controlled by artificial intelligence. E-commerce sales are increasing 20% year over year. During the COVID-19 pandemic additional retail volume shifted online and many customers became accustomed to sourcing essentials using e-commerce. This shift has put a greater burden on asupply chain infrastructure that has traditionally relied on human labor to pick, sort, pack, and process items for delivery. These manual processes are monotonous, error-prone, and sometimes dangerous, have extremely high worker turnover. The automation of these processes elevates worker roles and brings greater consistency to the processes. The innovation developed during this Phase II project may enable broader automation of complex materials handling processes by creating novel training systems for artificial intelligence-enabled robotic systems that are configured specifically for individual customer needs. This innovation may increase US supply chain resilience, enabling citizens to rapidly and reliably obtain necessities such as food, medicine, and health supplies without needing to leave their homes. The commercial opportunity is large, with over $20B spent on US pick and pack wages annually.This Small Business Innovation Research (SBIR) Phase II project seeks to develop new methods for rapidly training artificial intelligence (AI)-enabled robotic systems built for object identification and manipulation. Warehouse object manipulation tasks are variable and automating them often requires custom solutions for each customer and facility. These custom solutions are often prohibitively expensive. To solve these problems, an industrial operating system that can be deployed across many configurations of materials handling processes is required. This project aims to develop modules critical to scaling commercial deployments, such as quality control vision systems, automated assessments of item pickability, and enhanced AI systems for robotic picking. The anticipated result of this project is an industrial AI-enabled robotic operating system that allows rapid configuration of robotic systems to implement highly-optimized processes for picking and packing individual items in e-commerce logistics.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": "459", "attributes": { "award_id": "2205941", "title": "Collaborative Research: Conference: 2022 Secure and Trustworthy Cyberspace PI Meeting", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 911, "first_name": "Jeremy", "last_name": "Epstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2023-01-31", "award_amount": 35198, "principal_investigator": { "id": 912, "first_name": "Heather Richter", "last_name": "Lipford", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 248, "ror": "https://ror.org/04dawnj30", "name": "University of North Carolina at Charlotte", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 248, "ror": "https://ror.org/04dawnj30", "name": "University of North Carolina at Charlotte", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This award supports development of the program for a 2-day workshop to bring together PIs from across the Secure and Trustworthy Cyberspace (SaTC) program. Specific objectives of the PI meeting include:- to stimulate coordination and collaboration amongst SaTC PIs working on different projects;- to foster new collaborations between SaTC researchers and researchers in other disciplines;- to share experiences and learn from others' experiences in transitioning research into practice; and- to develop ideas and share methods for improving education, recruitment, and career development in cybersecurity.Building a strong community among SaTC researchers helps identify new research topics, avoid duplication of existing research, and improve educational opportunities for graduate students.Some elements of the meeting are structured so as to mitigate risks associated with the spread of COVID-19 or the virtual participation that its spread might necessitate.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": "458", "attributes": { "award_id": "2132195", "title": "RII Track-4: The Integration of Plasmonic Nanoantenna and Super-hydrophobic Surface for Ultrasensitive Fluorescence CRISPR Biosensing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 909, "first_name": "Chinonye", "last_name": "Nnakwe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2024-01-31", "award_amount": 180493, "principal_investigator": { "id": 910, "first_name": "Shengjie", "last_name": "Zhai", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 247, "ror": "", "name": "University of Nevada Las Vegas", "address": "", "city": "", "state": "NV", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 247, "ror": "", "name": "University of Nevada Las Vegas", "address": "", "city": "", "state": "NV", "zip": "", "country": "United States", "approved": true }, "abstract": "Early diagnosis provides significant and unprecedented benefits since patients diagnosed at an early stage of diseases often have a good chance for cure and functional outcomes. In addition, rapid testing is crucial to combat the pandemic as exemplified by the ongoing COVID-19 pandemic. This project aims to design a direct Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) based point of care diagnostic system without pre-amplification of viral genomes. It will allow ultralow and ultrasensitive detection of many diseases (e.g., cardiovascular diseases, cancer, and infectious diseases) at an early stage when the concentration of viral genomes in body fluids (i.e., urine, blood, saliva) is still very low and not sufficient to be detected by existing technologies. This NSF EPSCoR RII Track-4 fellowship provides the opportunity to collaborate with a renowned expert in point-of-care diagnostics for infectious diseases in the Department of Biomedical Engineering at the University of Connecticut to achieve this goal. The successful completion of this project will lead to noninvasive, inexpensive, mass-producible systems for early detection, treatment outcome evaluation of diseases, greatly improving patient morbidity and reducing healthcare cost, particularly important to Nevada, which consistently ranks near the bottom in terms of higher rates of the 12 leading causes of death.The objectives of the project are to (1) integrate nanoantenna with super-hydrophobic surfaces for enhancing the CRISPR/Cas12a detection sensitivity without pre-amplification; (2) integrate the designed enhanced CRISPR/Cas12a fluorescence detection module with microfluidics for viral detection in the blood sample. Although CRISPR based point of care diagnostic system has emerged as a popular technology and a powerful tool for rapid screening due to its simplicity and flexibility, it still has many limitations such as low stability in complex biological samples. One promising solution is to explore the nanoantenna technique to trigger the enhanced Localized surface plasmon. However, the nanoantenna technique is still far from being routinely implemented in biomedical fields due to a major obstacle not from plasmonics but from the mass transport: Most nanoantennas typically rely on diffusion to capture target molecules, which makes the detection time impractically long. This project integrates the nanoantenna with the superhydrophobic surface to address this diffusion limit. Droplets over super-hydrophobic surfaces maintain quasi spheres during evaporation and do not wet the surface. Therefore, the droplet evaporation replaces the diffusion and concentrates molecules onto the sensitive regions of the nanoantenna, becoming the dominant mechanism of mass transfer. The droplet evaporation time is not only much shorter than the diffusion time but also can be actively controlled, which is an additional benefit. The combination of plasmonics and super-hydrophobic surfaces offers a unique solution to the aforementioned key challenge and holds the promising for ultralow and ultrasensitive biosensing platforms enabled by CRISPR. The training and research experience provided by this RII Track-4 fellowship will allow the PI to successfully transition from the background of material science and engineering to a biomedical researcher.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": "457", "attributes": { "award_id": "2205940", "title": "Collaborative Research: Conference: 2022 Secure and Trustworthy Cyberspace PI Meeting", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 907, "first_name": "Jeremy", "last_name": "Epstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2023-01-31", "award_amount": 91601, "principal_investigator": { "id": 908, "first_name": "Michael", "last_name": "Reiter", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This award supports development of the program for a 2-day workshop to bring together PIs from across the Secure and Trustworthy Cyberspace (SaTC) program. Specific objectives of the PI meeting include:- to stimulate coordination and collaboration amongst SaTC PIs working on different projects;- to foster new collaborations between SaTC researchers and researchers in other disciplines;- to share experiences and learn from others' experiences in transitioning research into practice; and- to develop ideas and share methods for improving education, recruitment, and career development in cybersecurity.Building a strong community among SaTC researchers helps identify new research topics, avoid duplication of existing research, and improve educational opportunities for graduate students.Some elements of the meeting are structured so as to mitigate risks associated with the spread of COVID-19 or the virtual participation that its spread might necessitate.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": "456", "attributes": { "award_id": "2205939", "title": "Collaborative Research: Conference: 2022 Secure and Trustworthy Cyberspace PI Meeting", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 905, "first_name": "Jeremy", "last_name": "Epstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2023-01-31", "award_amount": 47158, "principal_investigator": { "id": 906, "first_name": "William H", "last_name": "Enck", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This award supports development of the program for a 2-day workshop to bring together PIs from across the Secure and Trustworthy Cyberspace (SaTC) program. Specific objectives of the PI meeting include:- to stimulate coordination and collaboration amongst SaTC PIs working on different projects;- to foster new collaborations between SaTC researchers and researchers in other disciplines;- to share experiences and learn from others' experiences in transitioning research into practice; and- to develop ideas and share methods for improving education, recruitment, and career development in cybersecurity.Building a strong community among SaTC researchers helps identify new research topics, avoid duplication of existing research, and improve educational opportunities for graduate students.Some elements of the meeting are structured so as to mitigate risks associated with the spread of COVID-19 or the virtual participation that its spread might necessitate.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": "455", "attributes": { "award_id": "2210823", "title": "Collaborative Research: CIF: Medium: Group testing for Real-Time Polymerase Chain Reactions: From Primer Selection to Amplification Curve Analysis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 903, "first_name": "Scott", "last_name": "Acton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 200000, "principal_investigator": { "id": 904, "first_name": "Venkatesan", "last_name": "Guruswami", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Group testing is a screening technique that relies on careful combinatorial mixing and testing of batches of samples. By using group testing instead of individual testing, for most problem settings of practical interest, one is guaranteed significant savings in the number of tests performed and consequently, significant reductions in reporting delays and experimental costs. Group testing is especially desirable when monitoring the spread of infectious diseases such as Covid-19, which requires frequent examinations of massive populations. Although many ad-hoc approaches to group testing for infectious diseases have been put forward, little work has addressed the problem of end-to-end group-testing protocol design, which includes the selection of genetic regions for viral/bacterial identification, mathematical modeling and analysis of the test results and the development of guiding protocols for communal testing strategies. The overarching goals of the project are to determine which group-testing methods can actually mitigate the spread of Covid-19 and other diseases and to what extent, to estimate the reduction in the number of infected individuals achievable through the use of pooled real-time polymerase chain reaction (RT-PCR) tests, and to aid in the employment of Mobile Testing Units that can reach geographically remote regions. Other broader societal impacts include increased readiness for fighting future pandemics and training a new cohort of young researchers on interdisciplinary topics involving machine learning, coding theory and bioinformatics. The project aims to develop specialized machine-learning, combinatorial and information-theoretic methods for (a) identifying genomic regions with predictably low-mutation rates that may be used as amplification primers for gold-standard real-time polymerase chain reactions (RT-PCR) and determining best mixing strategies based on the likelihood of infection; (b) developing adequate models for amplification curves generated by RT-PCR and corresponding test-errors; (c) formulating experimental-protocol-specific non-adaptive and adaptive semiquantitative group testing schemes that account for nonbinary test outcomes; (d) addressing the testing issues associated with high-viral load subjects and heavy-hitter communities; and (e) integrating the mathematical techniques developed into an agent-based model for disease spreading and control in order to assess the potential impact of group testing and recommend effective test-quarantine-retest strategies.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": "454", "attributes": { "award_id": "2144680", "title": "CAREER: Designing a Multi-Scale Framework for Trait Variation in Epidemiological Dynamics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 901, "first_name": "Henry", "last_name": "Warchall", "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": 100930, "principal_investigator": { "id": 902, "first_name": "Lauren M", "last_name": "Childs", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic demonstrates the devastation that infectious diseases can leave on modern society. Deciphering the dynamics of disease is essential to earlier and more effective interventions to mitigate disease spread. To fully understand these dynamics, information across systems that operate on multiple spatial and time scales, as well as evolving via feedback, must be combined. This research project aims to develop a mathematical framework to combine and analyze such information to further the understanding of infectious disease spread, providing insight for ongoing and future disease outbreaks. The results are also intended to be applicable to multi-scale questions in ecology and immunology more generally. The educational component will serve to train a diverse generation of interdisciplinary scientists, building the capacity of the STEM workforce. Through focused short courses aimed at providing specific, relevant skills in quantitative techniques and cross-disciplinary communication, graduate students in the mathematical and life sciences will learn to collaborate and communicate effectively. Training and mentoring of undergraduate and graduate students will take place through focused research projects and opportunities to garner translational skills, for example developing and implementing project-based work and participating in outreach activities. Additionally, interactive presentations will engage middle school students through exposure to cutting-edge mathematical biology research.The current COVID-19 pandemic highlighted the importance of interpretable, quantitative models that link mechanisms with data while accounting for variability. However, infectious disease dynamics remain incompletely understood, in part due to the lack of heterogeneity considered in models of immunological, ecological, and epidemiological aspects. Complex, non-linear feedbacks arise from heterogeneities; thus, novel quantitative frameworks are needed to better understand and control infectious disease. This project builds on work in ecology involving integral projection models to develop a framework incorporating trait-based variation. This framework aims to describe development of immunity in a population and, thus, determine disease risk over time. Results will be useful for monitoring infectious disease, selecting interventions, and informing public policy. The educational component aims to strengthen quantitative literacy to help produce a more interdisciplinary workforce. A group of graduate students, across applied, mathematical, and computational disciplines, will be introduced to fundamental quantitative skills such as statistical analyses, model building, parameter estimation, data visualization, and coding. Participants will connect with this material using a project-based approach with examples related to infectious disease dynamics.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": 1405, "count": 14046 } } }