Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1406&sort=-approved
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-approved", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-approved", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1407&sort=-approved", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-approved" }, "data": [ { "type": "Grant", "id": "12542", "attributes": { "award_id": "2236229", "title": "Exploring the Impact of Community Engagement on STEM Undergraduates via Math Circles for Urban Elementary School Students", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-02-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28471, "first_name": "Emily", "last_name": "Atieh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1113, "ror": "https://ror.org/02z43xh36", "name": "Stevens Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by fostering partnerships between higher education and local communities to improve K-12 math education while developing the teaching, leadership, and communication skills of undergraduates majoring in STEM fields. The project will develop and assess an innovative year-long community engagement program that prepares undergraduates to lead math circles with urban elementary school students in multiple community settings. Through exploration, collaboration, and problem solving, math circles promote positive perceptions of mathematics, children's ability to do math, and the relevance of math to everyday life. This project will advance understanding of the potential impact of math circles as a vehicle to enhance undergraduate STEM learning experiences. Undergraduate facilitators will be empowered to apply mathematics to effect positive change in their community. The project will also serve to broaden participation in math education, strengthen pathways to the STEM workforce, foster deeper understanding of important mathematical concepts, and build knowledge of best practices in teaching and learning.The project’s overarching goal is to develop an innovative community engagement program to harness the knowledge, skills, and enthusiasm of well-prepared STEM undergraduates to facilitate collaborative mathematics problem solving among elementary school students in their local community. Over three years, the project will design, implement, and iteratively improve a credit-bearing course that will prepare STEM undergraduates to lead math circles with elementary school children in various community settings: a school-based after school program, a public library, and the Boys and Girls Club. Four key tasks each year guide the project. First is to offer an elective community engagement course for all STEM majors. Second is to collaborate with partner sites to recruit elementary school students to participate in a math circle program. Third is to collect and analyze data to ascertain the impact on undergraduate math circle facilitators. The fourth and final task is to undertake formative and summative evaluation to strengthen outcomes for undergraduate facilitators and improve both the project-developed course and the overall community engagement program. The data gathered will provide a rich picture of who chooses to be a math circle facilitator and why, and the impact on students’ knowledge, skills, and perceptions of mathematics within and across community sites. Results will be disseminated via research publications, presentations at major conferences, webinars, a public-facing website, and a blueprint for replicating the project model at other institutions of higher learning. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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": "12543", "attributes": { "award_id": "2222220", "title": "MCA: Environmental Drivers of Snow Algae Bloom Dynamics, Physiology, and Life-Cycles", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Ecosystem Science" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-02-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28472, "first_name": "Robin", "last_name": "Kodner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 481, "ror": "https://ror.org/05wn7r715", "name": "Western Washington University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Snow algae, a group of photosynthetic microorganisms that are adapted to live in frozen habitats, are the major group of primary producers in alpine and polar snow ecosystems. These organisms have complex life cycles intimately connected to environmental conditions and seasonal habitat transformations. When snow algae bloom on the top of snow, their high biomass darkens snowfields due to red-colored protective pigments produced inside the algal cells. Snow algae blooms increase melting of snow and glaciers, promoting more growth due to the availability of liquid water, and resulting in a positive feedback loop. Despite their ecological importance, we still do not know how blooms form. This project is studying the dynamics of snow algae blooms by addressing the relationships between snow algae physiology, growth and reproduction, and their environment using field and laboratory-based experiments. This research is also providing a primarily undergraduate institution with a new, powerful instrument for simultaneously measuring photosynthesis and carbon fixation in algae that can be used in the field and facilitate field-based experiments. The results of this study build capacity for further studies that will help predict snow algal environment-biology interactions into the future.The connection between snow algal bloom dynamics, life cycle, and environmental conditions represents an opportunity to study ecosystem responses to climate warming in a tractable system. This project lays the foundation for characterizing the fundamentals of carbon fixation and primary productivity in snow algal blooms. It also increases our understanding of the fundamental role that snow algae physiology plays in the growth and reproduction of snow algae across life stages adapted to different habitats. The proposed field and laboratory methods will allow the development of an experimental design to simultaneously measure habitat conditions, primary productivity, and carbon fixation in natural populations and serves as a launch point for future studies and continued collaboration. The snow algal system presents an opportunity to study the evolution of climate-ecosystem feedbacks in environments threatened by significant habitat loss over the next century.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": "12544", "attributes": { "award_id": "2229975", "title": "Collaborative Research: CyberTraining: Pilot: Operationalizing AI/Machine Learning for Cybersecurity Training", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28473, "first_name": "Houbing", "last_name": "Song", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 315, "ror": "", "name": "Embry-Riddle Aeronautical University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "The interplay between AI and cybersecurity introduces new opportunities and challenges in the cybersecurity of AI as well as AI for cybersecurity. However, operations and configurations of AI cyberinfrastructure (CI) with a security mindset are rarely covered in the typical AI curriculum. To fill this gap, this project intends to develop hands-on training materials and provide mentored training for current and future research workforce in engineering and science-related disciplines. By transforming and integrating training materials into a course curriculum, this project aims to train potential cyberinfrastructure professionals in the CI community at large to handle AI with and for cybersecurity. This project has the potential to develop the research workforce in operating AI cyberinfrastructure with a security mindset to meet the national and economical needs and priorities of CI advancement. This project’s goal is to broaden the adoption of advanced cyberinfrastructure through training. This project develops a holistic technical approach for cybertraining: to identify, apply, and evaluate AI techniques which are inextricably related to well-defined operational cybersecurity challenges. The project intends to develop a Docker-based training platform that simulates and pre-configures a variety of scenarios to support hands-on AI cyberinfrastructure operations in the context of cybersecurity. Three levels of projects (exploratory, core, and advanced) are designed and integrated into the platform to help researchers and educators customize and develop into different education and training environments. The project democratizes the access and adoption of advanced AI cyberinfrastructure, while integrating cyberinfrastructure skills with the security mindset to foster inter-disciplinary and inter-institutional research collaborations. In addition to the dissemination through publications and social media, the outcomes from this project have the potential to benefit the greater cyberinfrastructure community and beyond, through the training and the sharing of the \"AI for and with cybersecurity\" course curriculum. This project is jointly funded by OAC and the CyberCorps program.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": "12545", "attributes": { "award_id": "2225445", "title": "GEM: Suprathermal and Energetic Ion Heating by Electromagnetic Ion Cyclotron Waves and Magnetosonic Waves in the Inner Magnetosphere", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "MAGNETOSPHERIC PHYSICS" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28474, "first_name": "Wen", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 168, "ror": "", "name": "Trustees of Boston University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The focus of this project is to study the ion heating by plasma waves in the Earth's magnetosphere. The electromagnetic ion cyclotron (EMIC) and magnetosonic waves are plasma waves generated by ring current ions in the magnetosphere. These plasma waves could scatter and heat the ions, transferring energy from the ring current to other regions or populations. This project investigates the ion heating process by combining simulations with Van Allen Probes observations. The modeling efforts will reveal the source of ions from wave-particle interaction. Understanding the ion dynamics will help future space missions traveling through the magnetosphere and implementation of the ion-wave interaction effects in space weather prediction. This project will help quantify the ion flux variations in the inner magnetosphere, where many satellites are operating to provide human's essential needs of communication, navigation, and security. This project supports two early-career scientists, a female faculty and a graduate student at Boston University. The Center for Space Physics at Boston University provides an ideal environment for collaboration in space science research, student training and teaching, community engagement, and outreach activities.This project aims to determine the conditions and consequences of resonant ion heating by EMIC and magnetosonic waves through comprehensive satellite observations and numerical modeling. Firstly, a survey of ion heating events will be performed using the Van Allen Probes data. The ion heating events will be categorized by different wave properties and background plasma parameters, to reveal the critical conditions that cause evident ion heating features in the inner magnetosphere. Secondly, a series of quasilinear simulations of the ion heating and scattering effects will be performed during the EMIC and magnetosonic wave events. The simulation inputs are provided by the wave properties and background plasma parameters from the survey to demonstrate the efficiency and energies of ion heating. Thirdly, since the amplitudes of EMIC and magnetosonic waves could be large so that they can break the quasi-linear approximation, the interaction between ions and large amplitude waves will be evaluated using a test particle code. A series of test particle simulations will be performed through varying wave amplitudes to find the threshold beyond which nonlinear effects become important. The ion phase trapping and bunching effects by strong EMIC and magnetosonic waves will also be evaluated to quantify their contribution to ion acceleration at different energies.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": "12546", "attributes": { "award_id": "2150093", "title": "Collaborative Research: REU Site: The Socio-Ecological Role of Greenways in Urban Systems - An Interdisciplinary Approach", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "RSCH EXPER FOR UNDERGRAD SITES" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28475, "first_name": "Shannon", "last_name": "McCarragher", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 532, "ror": "", "name": "Southern Illinois University at Edwardsville", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the Directorate for Social, Behavioral and Economic Sciences (SBE). The REU program has both scientific and societal benefits integrating research and education. This REU Site award to University of Tennessee Chattanooga, located in Chattanooga, TN, and Southern Illinois University Edwardsville in Edwardsville, IL, will support the training of 10 students for 10 weeks for three years. Research is conducted at Chattanooga, TN, Edwardsville, IL, and Cleveland, TN. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities or from an under-represented group, will be trained in the program. Students will learn how policy decisions are made and how interdisciplinary research is conducted, with many presenting the results of their work at scientific conferences and to local policymakers and community stakeholders. The research will integrate greenway networks into the broader field of urban science and improve our understanding of the environmental and human associated impacts of greenway networks in urban areas. Assessment of the program will be done through the online SALG URSSA tool. Students will be tracked after the program in order to determine their career paths.The theme for this 3-year research experience focuses on enhancing environmental resilience and sustainability by examining the interaction between human and natural systems within urban greenway networks. The research is grounded in three fundamental questions: 1) What are the human and ecological drivers of microclimate? 2) What are the human usage patterns in urban greenway networks? 3) How can empirical evidence on urban greenway dynamics inform the broader scientific community and local community stakeholders on ways to mitigate environmental impacts and social disparities in cities? Students will work in interdisciplinary teams and assess how greenways vary based on social and ecological characteristics of the greenway in each respective city. Results from each team will be combined into a larger dataset which will be used to assess greenway characteristics in varying city sizes. The findings will be shared with community leaders and stakeholders to inform them of the current impacts of their greenway system and potential opportunities for expanding the greenway network. Students will learn and apply skills related to: biodiversity; data collection, analysis, and visualization; geographic information systems (GIS); and, evidence-based policymaking. These skills will then be used to better understand the human and biological drivers of microclimate variation within greenway networks and inform policymakers as to the best ways to mitigate environmental impacts and social disparities in order to create more resilient cities.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": "12547", "attributes": { "award_id": "2230087", "title": "Collaborative Research: CyberTraining: Implementation: Medium: Cross-Disciplinary Training for Joint Cyber-Physical Systems and IoT Security", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28476, "first_name": "Ahmad", "last_name": "Taha", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Critical infrastructures, such as the power grid, water systems, and manufacturing plants, continue to be targeted by stealthy and debilitating cyber and physical attacks. These attacks not only hinder our national security but also jeopardize our economic prosperity. Several hurdles impede addressing the security of such critical assets, including the integration of new possibly vulnerable sensing technologies deep within such realms, in addition to the profound lack of relevant training experts from academia and both private and public sectors. Along the same line of thought, the shortage of empirical data originating from such realms, in conjunction with the complexity of such systems, further exposes the problem when facing the challenges of sophisticated state-sponsored attackers. To this end, this project serves NSF's mission to promote the progress of science by offering well-rounded training to research scientists coming from diverse related areas. The project puts forward multidisciplinary curricula in addition to catalyzing critical infrastructure training and research, while establishing active and actionable dissemination partnerships with numerous stakeholders, tangibly influencing the security of such interrelated, highly-important societal systems. The project also widely influences the training of women and minorities in these imperative cross-disciplinary areas across the US. The project uniquely curates contextualized, large-scale benign and malicious cyber and cyber-physical empirical data from real infrastructure systems to strongly enable hands-on training and research. The project then develops automated methodologies to annotate such data while indexing and sharing it with relevant research scientists to empower forward-looking research workforce development. The project also designs, delivers and integrates cross-disciplinary curricula, composed of undergraduate and graduate courses and a certificate program, dealing with evolving topics such as, physical modeling of system dynamics, related empirically driven data science applications, and joint operational security analytics. It also offers unique training opportunities with relevant private and public sector partners for both pre- and post-graduation trainees, rendered by capstone projects, internships, and competitive placement options. The project also designs and implements various security techniques, along with realistic emulation and simulation toolsets, to offer practical training expertise to researchers. The project utilizes virtualized lab setups to offer self-paced training of such developed training material, while achieving considerable outreach to relevant researchers across the US and beyond. The project fosters a community of impactful experts in the critical infrastructure security area to widely-disseminate such developed training materials and labs through coordinating and hosting yearly workshops at the collaborating institutions. The project is steered by an established program evaluation body that is composed of leading NSF Industry-University Research Partnership experts, pedagogy facilitators, and representative researchers from operational local and national training and research centers. This project is jointly funded by OAC and the CyberCorps program.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": "12548", "attributes": { "award_id": "2230025", "title": "CyberTraining: Implementation: Small: Infrastructure Cybersecurity Curriculum Development and Training for Advanced Manufacturing Research Workforce", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28477, "first_name": "Hongyue", "last_name": "Sun", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 422, "ror": "", "name": "SUNY at Buffalo", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This project addresses the problem of Cyberinfrastructure (CI) needs in Advanced Manufacturing and Industry 4.0 by aiming to host a 10-week summer program for undergraduate, graduate students and CI professionals. The project develops instructional materials and implements a new CyberTrainng project to train current and future Advanced Manufacturing (AM) research workforce members in CI cybersecurity. It exposes the participants to various challenges of CI centered cybersecurity in Industry 4.0, with a focus on the vital national need for well trained and highly knowledgeable AM researchers who are capable of solving real-world cybersecurity problems in emerging complex Industry 4.0 manufacturing systems. The training also includes the secure and safe adoption of CI centered computational tools in AM. The project has direct and long term impacts in both the public and private sectors by training the research workforce to address CI centered cybersecurity challenges in Industry 4.0. University at Buffalo (UB) has established solid and intellectually-engaging programs in the areas of AM and CI centered research areas. This project can form a new synergetic research and education program for the AM research workforce. Moreover, utilizing the collaboration with established diversity-promotion programs at UB and local industrial partners, this CyberTraining project can significantly increase the collective impact of the project, benefiting numerous graduate and undergraduate students from underrepresented groups and improving the diversity of the research workforce.The goal of this project is to bridge the gap between general cybersecurity education and CI centered Advanced Manufacturing research training and to prepare a group of future AM research workforce with advanced CI cybersecurity knowledge to undertake specific roles in their future careers. Currently, as computers，networks and intelligent devices penetrating every aspect of Industry 4.0, CI cybersecurity training is in high demand to cover materials that deeply incorporate discipline-oriented topics for modern CI-centered manufacturing environments beyond core literacy in computers/networks security. Specifically, the team aims to develop and provide an innovative training program, including a set of hands-on labs that introduce domain specific cybersecurity attack surfaces and mitigation techniques. The training skills are crucial to address the various cybersecurity challenges of Industry 4.0 systems in AM hardware (e.g., industrial controllers and power line), AM software (e.g., embedded firmware, CAD/CAM software, and supervisory control and data acquisition (SCADA) systems), AM communication networks (e.g., industrial IoTs, CAN bus) and other crucial CI components (e.g., new side channel discovery, IP theft/protection, quality control tools, supply chain software/database, and incident response strategies). Moreover, the project employs learning science principles, specifically the active learning and inquiry based learning strategies, that facilitate students' learning outcomes and provide formative feedback to instructors.This project is jointly funded by OAC and the CyberCorps program.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": "12549", "attributes": { "award_id": "2218996", "title": "OPP-PRF: Conjugate Experiment to Explore Magnetospheric Phenomena Via Spatial Sonification and Mixed Reality", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "ANT Astrophys & Geospace Sci" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28478, "first_name": "Kristina", "last_name": "Collins", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 327, "ror": "https://ror.org/046a9q865", "name": "Space Science Institute", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Magnetic field variations on the Earth’s surface can be used to remote sense and characterize electrical currents and plasma waves in the near-Earth space environment that can affect technology, for example by inducing currents in power grids. Asymmetries between the space environment in the polar regions of the northern and southern hemispheres can profoundly affect these magnetic field variations. Magnetometers, which measure the strength and direction of magnetic fields, have been installed in the Arctic and Antarctic at opposite ends of the Earth’s magnetic field lines. By looking at data from both sets of magnetometers, researchers can determine whether disturbances in the Earth’s magnetosphere (a region of near-Earth space dominated by the Earth’s magnetic field) caused by the Sun impact the Northern hemisphere, the Southern hemisphere or both, and thus understand the sources of north-south hemisphere asymmetries. Some events that appear in the magnetometer data may be difficult for computers to identify, but easy for people to identify if the data is translated into sound. Researchers will develop a tool for listening to data in a virtual reality environment, so that data from various instruments can be played back, making it easier to explore datasets intuitively. This system will be prototyped using a mixed reality headset for use in both science and education and may be used to analyze data taken at the same time by sensors on the ground and on satellites. This project will examine one particular type of disturbance – magnetosheath jets – and its relation to plasma waves by addressing the question “Do magnetosheath jets routinely drive Pc5/Pc6 geomagnetic pulsations?” via the analysis of magnetometer data from geomagnetically conjugate (based on the International Geomagnetic Reference Field, IGRF) Arctic and Antarctic magnetometers. This question will be approached first through traditional plotting and visual analysis, then by presenting datastreams as sound sources situated in a virtual audio environment developed in the Unity game engine and integrated with mixed reality presentation via the Microsoft Hololens platform. This approach will leverage human capabilities for spatial discrimination of sounds to identify geomagnetic pulsations (surface magnetic field variations related to plasma waves in outer space) related to magnetosheath jet events with potentially large north-south hemispheric asymmetries, spatially localized wave activity, and irregular waveforms. The resulting presentation modality will make use of existing repositories of magnetometer data and may potentially be extended to the presentation of synchronous datasets from multiple sensing 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": "12550", "attributes": { "award_id": "2230086", "title": "Collaborative Research: CyberTraining: Implementation: Medium: Cross-Disciplinary Training for Joint Cyber-Physical Systems and IoT Security", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28479, "first_name": "Peyman", "last_name": "Najafirad", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 240, "ror": "", "name": "University of Texas at San Antonio", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Critical infrastructures, such as the power grid, water systems, and manufacturing plants, continue to be targeted by stealthy and debilitating cyber and physical attacks. These attacks not only hinder our national security but also jeopardize our economic prosperity. Several hurdles impede addressing the security of such critical assets, including the integration of new possibly vulnerable sensing technologies deep within such realms, in addition to the profound lack of relevant training experts from academia and both private and public sectors. Along the same line of thought, the shortage of empirical data originating from such realms, in conjunction with the complexity of such systems, further exposes the problem when facing the challenges of sophisticated state-sponsored attackers. To this end, this project serves NSF's mission to promote the progress of science by offering well-rounded training to research scientists coming from diverse related areas. The project puts forward multidisciplinary curricula in addition to catalyzing critical infrastructure training and research, while establishing active and actionable dissemination partnerships with numerous stakeholders, tangibly influencing the security of such interrelated, highly-important societal systems. The project also widely influences the training of women and minorities in these imperative cross-disciplinary areas across the US. The project uniquely curates contextualized, large-scale benign and malicious cyber and cyber-physical empirical data from real infrastructure systems to strongly enable hands-on training and research. The project then develops automated methodologies to annotate such data while indexing and sharing it with relevant research scientists to empower forward-looking research workforce development. The project also designs, delivers and integrates cross-disciplinary curricula, composed of undergraduate and graduate courses and a certificate program, dealing with evolving topics such as, physical modeling of system dynamics, related empirically driven data science applications, and joint operational security analytics. It also offers unique training opportunities with relevant private and public sector partners for both pre- and post-graduation trainees, rendered by capstone projects, internships, and competitive placement options. The project also designs and implements various security techniques, along with realistic emulation and simulation toolsets, to offer practical training expertise to researchers. The project utilizes virtualized lab setups to offer self-paced training of such developed training material, while achieving considerable outreach to relevant researchers across the US and beyond. The project fosters a community of impactful experts in the critical infrastructure security area to widely-disseminate such developed training materials and labs through coordinating and hosting yearly workshops at the collaborating institutions. The project is steered by an established program evaluation body that is composed of leading NSF Industry-University Research Partnership experts, pedagogy facilitators, and representative researchers from operational local and national training and research centers. This project is jointly funded by OAC and the CyberCorps program.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": "12551", "attributes": { "award_id": "2219615", "title": "Collaborative Research: CISE-MSI: DP: IIS: Event Detection and Knowledge Extraction via Learning and Causality Analysis for Resilience Emergency Response", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Special Projects - CNS" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28480, "first_name": "Sanjay", "last_name": "Madria", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 504, "ror": "https://ror.org/00scwqd12", "name": "Missouri University of Science and Technology", "address": "", "city": "", "state": "MO", "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).This project utilizes information gleaned from social media about upcoming events to inform designated authorities in a timely manner so they can prepare mitigating action plans in case of emergency. Besides the extracted events themselves, harvested information may include (but is not limited to) images, posted messages, people’s sentiments and other surrounding context which will improve relevancy and trust of the information in understanding emergency situations. Extracted events become the source for investigating and analyzing spatial-temporal influences between events and cross-domain events to derive further insights. Potential applications are real-time tracking and monitoring of events for disaster relief, and forecasting of events for mitigation plans. Project outcomes will benefit researchers in information extraction and integration with interests in graph models and transfer learning; in addition to providing practical studying materials in areas such as deep learning, spatio-temporal data causality and analysis for students about disaster resilience and progressing towards community resilience in the long term. Moreover, the work will increase research capacity and collaborations to generate new research opportunities for students from underrepresented communities to pursue advanced degrees in computer science. Social media data provides a means to identify happening events prior, during, and post disasters. It provides signals for designed authorities for reactions and mitigation planning. This research will use social media posts, machine learning, and transfer learning techniques in three thrusts: 1) Extract local and global events; 2) Embed surrounding context such as relevance and trust; 3) Analyze spatial-temporal relationship between events and cross-domain events for further insights. This project puts forth a novel approach to events analysis under the umbrella of graph neural network and transfer learning, leveraging recent advances and opportunities in deep learning. The resulting data-driven algorithms will be modelled emphasizing the socio-economic aspects of the consequences and cascading losses by allowing the system to adapt according to the community-based variables and the dynamics of the disasters. The findings will be disseminated via publications, source code, and data to reach diverse communities of researchers and students.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": 1406, "pages": 1419, "count": 14184 } } }