Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-award_amount
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-award_amount", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-award_amount", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=-award_amount", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-award_amount" }, "data": [ { "type": "Grant", "id": "12630", "attributes": { "award_id": "2222482", "title": "Engineering Societies and the Lived Experiences of Marginalized Aspirants: (Re)imaging Inclusion", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Postdoctoral Fellowships" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28550, "first_name": "Kayla", "last_name": "Maxey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "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).The United States continues to struggle with achieving diversity in science, technology, engineering, and mathematics (STEM) fields. To address this challenge, U.S. STEM education has seen an increase in programs designed to broaden the participation of students from marginalized communities. A number of these programs aim to support the success of individual students but leave out examinations of the larger, social barriers to STEM participation. For example, engineers who are Black, Indigenous, People of Color, or gender diverse often describe engineering classrooms and workplaces as difficult and unwelcoming. In engineering fields, professional societies can provide an additional layer of support where identity difference is expected, acknowledged, and celebrated. This study is designed to examine connections between the lived experiences of diverse individuals, the engineering profession, and contemporary diversity programming. As the U.S. science, technology, engineering, and mathematics (STEM) education sector continues to grapple with the challenges of advancing diversity, equity, and inclusion in academic and employment sectors, these sectors have experienced rapid increases in broadening participation efforts over recent decades. These efforts have aspired to multiple outcomes including increased access, belonging, and retention of students from marginalized communities. However, these efforts focus largely on the experiences of individual students and have left systemic barriers, such as inequitable distributions of resources and discriminatory cultures, less well studied. In engineering fields, professional societies including the National Society of Black Engineers, Society of Women Engineers, and American Indian Science and Education Society serve as centers of empowerment, belonging, mentoring, and development where identity difference is understood, acknowledged, and celebrated. The PI will conduct a series of case studies to address two research questions: (Q1) What roles have engineering societies played in the delineation of racial, ethnic, and gender ideologies of engineering?; and (Q2) How do engineering societies influence the lived experiences of engineering aspirants and professionals of marginalized communities? The study is designed to examine the intentions and impacts of engineering societies to characterize the relationship between the lived experiences of individuals, academic and industry structures, and contemporary diversity, equity, and inclusion programming. The project will augment ongoing research through the Engineering PLUS Alliance's Continuous Improvement Data, Evaluation, and Research (CIDER) unit, an NSF INCLUDES funded initiative. The project responds to the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field.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": "12631", "attributes": { "award_id": "2324754", "title": "CAREER: Atmospheric Electricity on Earth and Mars", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "AERONOMY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28551, "first_name": "Jeremy", "last_name": "Riousset", "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": "Naturally occurring lightning in the Earth atmosphere releases tremendous amount of energy in a very short time. Lightnings most commonly occur during thunderstorms as electrostatic charges accumulate in clouds. Lightnings have also been found to occur on Mars, believed to be created by dust storms. Though not as common as on Earth because of low atmosphere pressure, such lightnings can be a hazard to instruments on Mars with metal objects acting like a lightning rod. The multiplication of robotic explorers at the surface of the planet has increased the chances of such electric discharges, increasing risks for instruments and an ever-more likely human-exploration.This CAREER research outlines a five-year effort to study the physics and observability of electrical discharges in atmospheric conditions representative of Earth and Mars. The investigators suggest a comprehensive study combining theory and experiments centered on discharges produced in air at pressures ranging from 6 to 1013 mbar, to examine the differences between discharges started from a hot, cylindrical or spherical electrode, and the electrification observed in the tribocharging of regoliths and sand grains. The principal objective is to further our understanding of the physics of electrical discharge in diverse environments. In particular, this research seeks to resolve the following outstanding issues in planetary electricity: (1) Can geometric factors adequately explain the difference between theoretical and observed lightning initiation thresholds? (2) Can modeling help assess the nature (glow, streamer, leader) of atmospheric breakdown occurring in the form of Transient Luminous Events or putative Martian lightning? (3) Can tribocharging lead to the initiation of such non-conventional discharges?The research plan aims to: • produce the formulation of a new, generalized model of electron avalanche initiated from a hot cylindrical or spherical electrode,• create a 3-D fractal models of extraterrestrial discharges and estimates of their electric charges and dipole moments,• make quantitative measurements of the electrification in a scaled Martian dust event,• disseminate of academic research outside academia through Astronomy on Tap talks, and• create a summer camp using LEGO Mindstorms to introduce middle-schoolers to programing and space science through an innovative and engaging approach.The success of this project will directly impact the design of future instruments for the detection of extraterrestrial atmospheric electricity by identifying the most measurable changes due to non-conventional lightning. It will also help to assess the risk of initiating discharges from surface objects in particular in the framework of Martian exploration. It will strengthen the relationship between academic research and the local community, through the 5-day summer camp, public lectures, and talks at informal venues. Through these tasks, the investigator will reach audiences of all ages and levels and seek to inspire the next generation of scientists and engineers.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": "12632", "attributes": { "award_id": "2146448", "title": "CAREER: Empowering White-box Driven Analytics to Detect AI-synthesized Deceptive Content", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Secure &Trustworthy Cyberspace" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28552, "first_name": "Shuang", "last_name": "Hao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Artificial intelligence (AI) synthesis techniques that automatically produce realistic images, videos, and other content have significantly improved over the past few years. Although there are promising legitimate applications of these techniques, they also raise serious trust and security threats. Cybercriminals increasingly weaponize AI synthesis techniques to deceive users and manipulate opinions without having to invest heavily in manual content generation. For instance, AI-synthesized profile photographs are abused to create fake accounts, while deepfake videos that simulate real people can give cybercriminals the ability to defame or impersonate others. Existing detection work mostly relies on \"black-box\" approaches that analyze content without considering the way the AI synthesis techniques work. This project's goal is to use \"white-box\" methods that consider how the techniques work, both to systematically detect AI-synthesized content, and to outline general principles that underlie how broad classes of AI synthesis algorithms work that will help detection algorithms adapt as new synthesis techniques are developed. The results of this research will reinforce user trust in online content and help social media sites and other Internet platforms mitigate deception through AI-synthesized content. The project team will integrate the new datasets and techniques developed in this research into undergraduate and graduate courses as well as online exercises to train future cybersecurity workers. The team will also support diverse participation in the research, actively recruiting and mentoring women and people from other under-represented groups.This research aims to advance AI synthesis detection in terms of efficacy, generalizability, and robustness. The work focuses on detecting AI-synthesized images and videos, as humans are more likely to be attracted to and deceived by visual content. The developed analytics principles are envisioned to inspire new work in these areas and expand to detection of other types of AI-synthesized content. The project is organized around three research thrusts. First, the team will develop a unified analytic framework to systematically dissect AI-synthesis models and gain deep understanding of synthesis patterns common across the models. Second, based on these findings, the team will design generalizable approaches based on the frequency and pixel domains to efficiently detect AI-synthesized images and videos and operate at scale. Third, it will enhance detection robustness by proactively investigating adversarial evasion strategies and prioritizing detection techniques resistant to those strategies. The framework and the developed techniques will be thoroughly evaluated with large-scale real-world data. This research will contribute to establishing a principled detection paradigm and provide insights to prevail over future forms of AI-based deception and propaganda.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": "12633", "attributes": { "award_id": "2229138", "title": "SHINE: Understanding the Physical Connection of the in-situ Properties and Coronal Origins of the Solar Wind with a Novel Artificial Intelligence Investigation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "SOLAR-TERRESTRIAL" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28553, "first_name": "Enrico", "last_name": "Landi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "Understanding the solar wind is crucial to space weather science and forecasting because the properties of the solar wind plasma affect the local conditions in the space environment around Earth. These conditions are largely the result of the speed, structure, and magnetic fields carried by the solar wind plasma. This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the solar wind, through research that utilizes modern Artificial Intelligence (AI), Machine Learning (ML) and big data analysis algorithms to analyze space-based and NSF-funded ground based coronagraph observations. The project is led by an early career female scientist and is cross-disciplinary, building a collaboration between solar physicists and data scientists. Graduate and undergraduate student researchers from under-represented groups in STEM will be supported.The project is a four-year research program that applies state-of-the-art AI/ML technology to in-situ solar wind measurements from past, current, and future missions. The goal is to classify solar wind types and to determine their coronal source regions, to understand the physical connection between the solar wind’s in-situ properties and their coronal origins. The team will use available observations from NASA space-based missions including the Advanced Composition Explorer, Wind, Parker Solar Probe, Ulysses and when available, Solar Orbiter (SO) 1. Spectroscopic data from the Solar and Heliospheric Observatory, Solar Terrestrial Relations Observatory, Hinode, Solar Dynamics Observatory and SO will provide magnetic field geometry and basic plasma diagnostics of the solar wind source regions. Furthermore, NSF-funded ground based coronagraphs such as CoMP (2011-2018), MK4 (1998-2013), KCor (2013-today) and, when available, UCoMP2 will be used to provide additional solar context data and plasma diagnostics.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": "12634", "attributes": { "award_id": "2225121", "title": "Collaborative Research: GEM--Energetic Electron Nonlinear Interactions with Oblique Whistler-Mode Chorus Waves", "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": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28554, "first_name": "Lunjin", "last_name": "Chen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Whistler-mode \"chorus\" waves are naturally occurring radio emissions in Earth's outer radiation belt. These waves can resonantly interact with trapped energetic electrons (~10 keV to >1 MeV), both accelerating them to high speeds and scattering them into the atmosphere as \"particle precipitation.\" Intense chorus waves, propagating at oblique angles to the background magnetic field, invoke such resonances in multiple harmonics and produce strong electron responses on rapid timescales, within tens of seconds. However, the role of different resonance harmonics in shaping the global state of the radiation belt remains unknown. This investigation will study the interactions between electrons and these intense oblique chorus waves to address this problem. Understanding the properties and mechanisms of these processes is important for forecasting space weather and its effects on sensitive systems, such as spacecraft surfaces and electronics. This project will also support early-career scientists and a graduate student, and the findings will be incorporated into public outreach materials and undergraduate classes at the University of Texas at Dallas.The goal of this project is to address three specific scientific questions: 1) How does energetic electron phase space density evolve under nonlinear wave-particle interactions with oblique whistler-mode chorus waves? 2) Are there observable distinctions between different electron harmonic resonances with magnetospheric chorus waves and, if so, what are they? 3) What are the respective roles of Landau and cyclotron resonances in shaping the outer radiation belt in events with intense oblique chorus waves? To answer these questions, the investigators will develop numerical models of both oblique chorus wave packets and electron phase space density evolution, and quantitatively compare model results with in-situ measurements made by NASA's Van Allen Probes. Importantly, this research may lead to the discovery of direct observational evidence of electron nonlinear resonances in distinctive harmonics, including Landau resonance. These important processes have been theorized to occur in outer space but have yet to be confirmed with experimental evidence.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": "12635", "attributes": { "award_id": "2303650", "title": "CRII: RI: A Deep Gameplay Framework for Strong Story Experience Management", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Robust Intelligence" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28555, "first_name": "Joseph", "last_name": "Robertson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 510, "ror": "", "name": "Rochester Institute of Tech", "address": "", "city": "", "state": "NY", "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).Experience managers are intelligent agents that produce personalized stories that change based on decisions a player makes in a digital game. These agents create new and powerful types of interactive stories for art and entertainment, training applications, and personalized education. A central problem for experience managers is avoiding dead ends, which are situations where story structure is broken due to a choice by the player. This problem results in experiences with un-interesting stories, missed training sequences, and long spells without appropriate targeted educational content. This project will develop a novel experience management architecture to quickly navigate around dead end situations during real-time interaction with a human participant. The architecture is based on recent advances in deep reinforcement learning for general game playing. This experience management platform will enable new forms of real-time training and education applications.This work addresses a fundamental gap in existing experience managers that do not adversarially plan against sequences of player actions that lead to dead end situations. The specific research objectives are to create a deep reinforcement learning-based gameplay agent platform that (1) builds state spaces described by an action language, (2) identifies dead end states without performing exhaustive search, (3) relaxes assumptions of zero-sum and symmetric gameplay, and (4) solves narrative planning problems by compiling specialized narrative reasoning into a standard action language domain description. If successful, this research will significantly improve the speed and control of experience management agents and provide a pipeline to controlling existing and future specialized interactive narrative formalisms. These improvements will allow control of larger and more immersive narrative, training, and pedagogical environments compared to current 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": "12636", "attributes": { "award_id": "2219703", "title": "Collaborative Research: CISE-MSI: DP: CPS: Cyber Resilient 5G Enabled Virtual Power System for Growing Power Demand", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CISE MSI Research Expansion" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28556, "first_name": "Ayush", "last_name": "Goyal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1321, "ror": "", "name": "Texas A&M University-Kingsville", "address": "", "city": "", "state": "TX", "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).The demand for clean power is on the rise globally. The United States Department of Energy has projected that the percentage of clean electricity generated by solar power will increase to 33% to compete with the ever-growing electricity demand in the United States (US) by the year 2050. To minimize the upgradation of the existing substation and source infrastructure, it is favorable to integrate a solar energy source aided with battery storage system at the distribution level. The combination of these energy sources can be managed efficiently to behave like a \"single utility-scale power station”. This is the concept of Virtual Power Plant (VPP). This project aims to address the implementation of cyber-secure pole mounted solar and battery systems equipped with smart controllers to provide a framework to remotely control and optimize the system to provide a solution to the growing power demands. Furthermore, by employing and mentoring students from underrepresented backgrounds in STEM, this project will aim at bridging the gap in institutions across the US. It will train the next generation of scholars from minority serving universities and marginalized communities in the fields of cybersecurity, utilization of renewable resources, and machine learning to address the pressing problems of this age.The technical aspect of the project aims to design and implement a 5G-enabled, cyber resilient smart Artificial Intelligence (AI) based microinverter for a pole-mounted solar power system with an energy storage system connected to the low-voltage distribution networks to operate as a VPP. The data collected from the smart microinverter will be used to train a predictive model to better manage the system for improved performance. A secure and privacy-preserving 5G based communication protocol will allow uninterrupted and secure data flow between the photovoltaic and the battery system, the smart microinverter, and the Supervisory Control and Data Acquisition (SCADA) system. Hence, a security framework based on Machine Learning (ML) models will be designed and it will be based on prior cyber-attack datasets and will include continuous learning using the data collected from smart controllers and the SCADA system used to detect cyber-attacks and exploring mitigation solutions for 5G-enabled SCADA-controlled VPP network system. Guidelines and procedures will be designed to help secure the physical systems while ensuring privacy and data protection by alerting administrators regarding security compromises of the VPP network to mitigate the risks and attacks.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": "12637", "attributes": { "award_id": "2219701", "title": "Collaborative Research: CISE-MSI: DP: CPS: Cyber Resilient 5G Enabled Virtual Power System for Growing Power Demand", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CISE MSI Research Expansion" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28557, "first_name": "Kanwalinderjit", "last_name": "Kaur", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2124, "ror": "", "name": "CSUB Auxiliary for Sponsored Programs Administration", "address": "", "city": "", "state": "CA", "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).The demand for clean power is on the rise globally. The United States Department of Energy has projected that the percentage of clean electricity generated by solar power will increase to 33% to compete with the ever-growing electricity demand in the United States (US) by the year 2050. To minimize the upgradation of the existing substation and source infrastructure, it is favorable to integrate a solar energy source aided with battery storage system at the distribution level. The combination of these energy sources can be managed efficiently to behave like a \"single utility-scale power station”. This is the concept of Virtual Power Plant (VPP). This project aims to address the implementation of cyber-secure pole mounted solar and battery systems equipped with smart controllers to provide a framework to remotely control and optimize the system to provide a solution to the growing power demands. Furthermore, by employing and mentoring students from underrepresented backgrounds in STEM, this project will aim at bridging the gap in institutions across the US. It will train the next generation of scholars from minority serving universities and marginalized communities in the fields of cybersecurity, utilization of renewable resources, and machine learning to address the pressing problems of this age.The technical aspect of the project aims to design and implement a 5G-enabled, cyber resilient smart Artificial Intelligence (AI) based microinverter for a pole-mounted solar power system with an energy storage system connected to the low-voltage distribution networks to operate as a VPP. The data collected from the smart microinverter will be used to train a predictive model to better manage the system for improved performance. A secure and privacy-preserving 5G based communication protocol will allow uninterrupted and secure data flow between the photovoltaic and the battery system, the smart microinverter, and the Supervisory Control and Data Acquisition (SCADA) system. Hence, a security framework based on Machine Learning (ML) models will be designed and it will be based on prior cyber-attack datasets and will include continuous learning using the data collected from smart controllers and the SCADA system used to detect cyber-attacks and exploring mitigation solutions for 5G-enabled SCADA-controlled VPP network system. Guidelines and procedures will be designed to help secure the physical systems while ensuring privacy and data protection by alerting administrators regarding security compromises of the VPP network to mitigate the risks and attacks.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": "12638", "attributes": { "award_id": "2219700", "title": "Collaborative Research: CISE-MSI: DP: CPS: Cyber Resilient 5G Enabled Virtual Power System for Growing Power Demand", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CISE MSI Research Expansion" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28558, "first_name": "Sagnika", "last_name": "Ghosh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 376, "ror": "https://ror.org/01fpczx89", "name": "Tennessee State University", "address": "", "city": "", "state": "TN", "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).The demand for clean power is on the rise globally. The United States Department of Energy has projected that the percentage of clean electricity generated by solar power will increase to 33% to compete with the ever-growing electricity demand in the United States (US) by the year 2050. To minimize the upgradation of the existing substation and source infrastructure, it is favorable to integrate a solar energy source aided with battery storage system at the distribution level. The combination of these energy sources can be managed efficiently to behave like a \"single utility-scale power station”. This is the concept of Virtual Power Plant (VPP). This project aims to address the implementation of cyber-secure pole mounted solar and battery systems equipped with smart controllers to provide a framework to remotely control and optimize the system to provide a solution to the growing power demands. Furthermore, by employing and mentoring students from underrepresented backgrounds in STEM, this project will aim at bridging the gap in institutions across the US. It will train the next generation of scholars from minority serving universities and marginalized communities in the fields of cybersecurity, utilization of renewable resources, and machine learning to address the pressing problems of this age.The technical aspect of the project aims to design and implement a 5G-enabled, cyber resilient smart Artificial Intelligence (AI) based microinverter for a pole-mounted solar power system with an energy storage system connected to the low-voltage distribution networks to operate as a VPP. The data collected from the smart microinverter will be used to train a predictive model to better manage the system for improved performance. A secure and privacy-preserving 5G based communication protocol will allow uninterrupted and secure data flow between the photovoltaic and the battery system, the smart microinverter, and the Supervisory Control and Data Acquisition (SCADA) system. Hence, a security framework based on Machine Learning (ML) models will be designed and it will be based on prior cyber-attack datasets and will include continuous learning using the data collected from smart controllers and the SCADA system used to detect cyber-attacks and exploring mitigation solutions for 5G-enabled SCADA-controlled VPP network system. Guidelines and procedures will be designed to help secure the physical systems while ensuring privacy and data protection by alerting administrators regarding security compromises of the VPP network to mitigate the risks and attacks.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": "12639", "attributes": { "award_id": "2221276", "title": "Driving Math Competency through STEM Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28559, "first_name": "Shanah", "last_name": "Grant", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 727, "ror": "https://ror.org/05mpwj415", "name": "Fort Valley State University", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Fort Valley State University (FVSU). As a Historically Black College and University (HBCU), FVSU has over 86% low-income students. Over its 6-year duration, this project will fund scholarships to 30 unique full-time students who are pursuing bachelor’s degrees in Mathematics. Specifically, 12 first-year students will receive up to four-year scholarship support, while 18 third year/transfer students will receive up to two-year scholarship support. The project aims to prepare low-income, high achieving students not only to obtain a degree in mathematics, but also to gain exposure to STEM modeling through summer research. STEM modeling will enable students to be strong candidates for the high demanding STEM workforce and/or graduate STEM degrees. This project aims to increase student persistence in STEM fields by linking scholarships with effective supporting activities, including self-regulated learning, growth mindset, mentoring, undergraduate research experiences, professional development, and participation in discipline-specific conferences. Over the six years duration, this project intends to investigate the ways in which the latter factors affect persistence for each cohort. Because Fort Valley State University has a high population of underrepresented students, this project has the potential to broaden participation in STEM fields. Moreover, this project will assist in the preparation of underrepresented students to become outstanding STEM professionals, and researchers who are able to solve real world problems with mathematics. The findings of the project will help to improve the retention rate of low-income, academically talented students in STEM.The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Four goals animate this project. First is to retain math students’ enrollment into subsequent year or increase graduation rate from 57% to 80%. Second is to improve students’ level of self-regulation. Third is to promote a growth mindset in students. Fourth and finally is to improve mathematics identity among students. This will be done through the dissemination of scholarship application information, visits to high schools, cohort-building activities, faculty mentoring, professional development, and student support workshops. In addition, instructional activities focusing on cognitive, metacognitive, and self-management strategies; activities to foster growth mindsets and regular engagement with mathematics activities; faculty mentoring and the use of case studies; along with provision of career information will be coordinated by the project PIs. Though studies have been done to describe the independent effects of self-regulation, having a growth mindset, and professional development on African American college students, little is known on how a combination of all three components affect black low-income math students. This project will investigate the collective impact of self-regulation, having a growth mindset, and professional development on the persistency of black low-income math students. To evaluate the effectiveness of the project, the Center for Evaluation and Research Services (CERS) at Georgia State University will analyze pre-/mid/post- assessment data regarding self-regulation and mindset and will collect and analyze narrative data regarding participants' experiences in the program and identification with mathematics. The results of the project will be disseminated through professional local, regional and/or national conferences and/or journal publications. Insight from the project will also be shared with the necessary departments on campus through faculty presentations. This project is funded by the NSF Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income 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": 1385, "pages": 1419, "count": 14184 } } }