Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&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=1442&sort=-award_amount", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1406&sort=-award_amount", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1404&sort=-award_amount" }, "data": [ { "type": "Grant", "id": "12597", "attributes": { "award_id": "2219712", "title": "Collaborative Research: CISE-MSI: DP: IIS: Hybrid-Architecture Symbolic Parser with Neural Lexicon", "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": 28519, "first_name": "Jose", "last_name": "Vega-Riveros", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1024, "ror": "", "name": "University of Puerto Rico Mayaguez", "address": "", "city": "", "state": "PR", "zip": "", "country": "United States", "approved": true }, "abstract": "An utterance is grammatical if it conforms to the speaker's mental grammar. But grammatical utterances may be ambiguous. Ambiguity, the assignment of multiple representations and interpretations for one utterance, is an undesirable byproduct of language use for efficient communication; nevertheless, ambiguity is pervasive in language performance, since mental linguistic representations have a hierarchical structure, yet language externalization is linear due to sensory-motor constraints. This Hybrid-Architecture Symbolic Parser and Neural Lexicon system (HASPNeL) judges whether a given utterance is grammatical and detects whether it is ambiguous at the word or sentence level. HASPNeL is also a computational cognitive model of human language following current syntactic theory on Minimalist grammars, which must satisfy conditions of learnability, evolvability, and universality. Although the advantages of a hybrid symbolic-probabilistic architecture have been documented in relevant literature, there is not yet any comparable system based upon this architecture. HASPNeL is expected to impact the development of applications for education and industry (particularly applicable to underrepresented languages for which there is not enough available data), and to further research and advancement of human language cognitive models and technologies. HASPNeL's hybrid architecture comprises a feature-unification parser and structure generator, which is encoded using a symbolic AI approach, and a machine learning tagger that is used to construct a feature-enriched lexicon. An annotated synthetic corpus trains a neural network system that properly identifies and tags each lexical item and estimates the likelihood of each category within the corpus. To account for lexical ambiguity, tokens with different categories, features or meanings are assigned different entries. The system only parses grammatical utterances, recognizes ambiguous utterances by producing as many syntactic representations as there were possible interpretations, and calculates the likelihood for each structural description. HASPNeL is able to account for syntactic variation by minor parametric adjustments to grammatical and lexical features.This project is jointly funded by MSI and the Established Program to Stimulate Competitive Research (EPSCoR).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": "12598", "attributes": { "award_id": "2219713", "title": "Collaborative Research: CISE-MSI: DP: IIS: Hybrid-Architecture Symbolic Parser with Neural Lexicon", "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": 28520, "first_name": "Sandiway", "last_name": "Fong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 438, "ror": "https://ror.org/03m2x1q45", "name": "University of Arizona", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "An utterance is grammatical if it conforms to the speaker's mental grammar. But grammatical utterances may be ambiguous. Ambiguity, the assignment of multiple representations and interpretations for one utterance, is an undesirable byproduct of language use for efficient communication; nevertheless, ambiguity is pervasive in language performance, since mental linguistic representations have a hierarchical structure, yet language externalization is linear due to sensory-motor constraints. This Hybrid-Architecture Symbolic Parser and Neural Lexicon system (HASPNeL) judges whether a given utterance is grammatical and detects whether it is ambiguous at the word or sentence level. HASPNeL is also a computational cognitive model of human language following current syntactic theory on Minimalist grammars, which must satisfy conditions of learnability, evolvability, and universality. Although the advantages of a hybrid symbolic-probabilistic architecture have been documented in relevant literature, there is not yet any comparable system based upon this architecture. HASPNeL is expected to impact the development of applications for education and industry (particularly applicable to underrepresented languages for which there is not enough available data), and to further research and advancement of human language cognitive models and technologies. HASPNeL's hybrid architecture comprises a feature-unification parser and structure generator, which is encoded using a symbolic AI approach, and a machine learning tagger that is used to construct a feature-enriched lexicon. An annotated synthetic corpus trains a neural network system that properly identifies and tags each lexical item and estimates the likelihood of each category within the corpus. To account for lexical ambiguity, tokens with different categories, features or meanings are assigned different entries. The system only parses grammatical utterances, recognizes ambiguous utterances by producing as many syntactic representations as there were possible interpretations, and calculates the likelihood for each structural description. HASPNeL is able to account for syntactic variation by minor parametric adjustments to grammatical and lexical features.This project is jointly funded by MSI and the Established Program to Stimulate Competitive Research (EPSCoR).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": "12599", "attributes": { "award_id": "2328485", "title": "Collaborative Research: Roles of rupture complexity, geological structure, stress interaction on earthquake sequences", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "XC-Crosscutting Activities Pro" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28521, "first_name": "Xiaowei", "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": 342, "ror": "https://ror.org/01f5ytq51", "name": "Texas A&M University", "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).Earthquakes remain one of the most significant natural hazards facing society. With the increase of human-induced seismicity, regions that previously had lower seismic risk (e.g., central United States) are now facing earthquake-related hazards. The dramatic increase in the seismicity rate during the past decade in central US, and the improved instrumentation here and in California provide a rich dataset of well-recorded small-to-moderate sized earthquakes in two very different geologic settings; one dominated by tectonic motion on the great San Andreas fault, and the other where human activities induced earthquakes on less continuous, smaller, faults. This provides the opportunity to learn more about the controlling factors and consequences of earthquakes in both settings. By studying and comparing the earthquakes and their interactions in two very differently deforming regions (Northern California and Oklahoma), this project will advance our understanding of the fundamental processes of earthquake physics, their dependence on tectonic setting, and help reduce earthquake related hazards. An improvement in understanding of the relationship between earthquake rupture processes and hazard parameters (seismicity and ground motion) could help to reduce seismic hazard posed to local communities and important infrastructure. This analysis will also combine earthquake and industry 3D seismic data, which will help to develop guidelines to identify potentially hazardous critically stressed faults in Oklahoma. The project includes mentoring and collaboration on a range of levels, contributing to the education of two graduate students (at OU) and support for an early-career PI (at OU). The derived state-of-the-art database of earthquake catalog, cluster characteristics and source parameters will provide fundamental input for many studies, and will be of broad interest to earthquake hazard community, and understanding of earthquake physics. The methods and workflow will feed into activities for classes in earthquake and exploration seismology, and structural geology. The complexity of earthquakes is a significant factor governing earthquake source dynamics and the consequent ground motions, and it is related to complexity in the fault structures on which the earthquakes occur. This project will use both high-quality earthquake seismograms and industrial 3D seismic data, and develop improved techniques to quantify complexity and enhance our understanding of the fundamental earthquake source process on a variety of temporal and spatial scales. The researchers focus on the inherent earthquake source variability, its relationship with geologic structure, and its influence on earthquake sequence evolution and ground motion patterns. They perform a combined spectral and time domain analysis of earthquake sources and geological fault structure in two distinct tectonic settings: northern California which is dominated by the San Andreas plate-boundary fault, and Oklahoma where human activities are inducing earthquakes in a low-strain rate region. Spectral complexity and source-time-function complexity will be quantified, and automatic classification methods will be developed to identify complex earthquakes. Collocated high-resolution industrial 3D seismic data and seismicity enables us to better understand characteristics of seismogenic faults in the basement in detail. The synthesis and integration of multiple datasets will help to understand the interlink between geologic structure and earthquake rupture.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": "12600", "attributes": { "award_id": "2222278", "title": "Examining Undergraduate Research Experiences of Latinx STEM Students in Research Intensive HSIs and Emerging-HSIs", "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": 28522, "first_name": "Katherine", "last_name": "Garcia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 177, "ror": "", "name": "University of California-Irvine", "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). This postdoctoral fellowship research project will undertake a comparative study of undergraduate research experiences for Latinx students at Hispanic Serving Institutions (HSIs) using an asset-based framework and Chicana/Latina feminist epistemology and the \"platicas methodology\" (or informal conversations). The study is expected to provide insights about the campus context of HSIs that influence STEM retention of Latinx students through their participation in undergraduate research experiences. In addition to conducting a research project, the fellow will implement a professional development plan designed to expand her methodological training in quantitative (social network analysis) and mixed methods research design and to gain pedagogical and mentoring tools to work with diverse students. The fellow’s future research directions include investigating the intersectional identities of Latinx students in STEM and exploration of STEM graduate pathways that embrace Latinx culture. The study aims to examine the social and cultural assets that Latinx use in two research-intensive HSI settings. Guided by asset-based frameworks, Rendon’s Latinx STEM Success Model, and the Community-Centered STEM Identity Model, the project will employ both a qualitative culturally relevant methodology known as \"platicas,\" and a quantitative methodology based in social network analysis. A transformative design was chosen, as this study aims to use culturally relevant methods with a marginalized student group (Latinx students in STEM) with the goal of transforming STEM outcomes. In addition, a Chicana/Latina Feminist epistemology will provide an overarching framework for the study. The study provides an intersectional approach by centering Latinx STEM students who are also first-generation college students. It line of inquiry holds potential to provide insights into the unique campus context of HSIs in advancing STEM retention through participation in undergraduate research experiences.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 project is partially funded by The Kaleta A. Doolin Foundation through a partnership with the National Science Foundation to promote greater diversity within the STEM/STEM education research workforce.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": "12601", "attributes": { "award_id": "2142514", "title": "Distributed Learning for Undergraduate Programs in Data Science at Diverse Universities", "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": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 11646, "first_name": "Hong", "last_name": "Liu", "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": "This project aims to serve the national interest by improving undergraduate education in data science. This project will develop and deliver ten Data Sciences (DS) courses to students from a consortium of eleven diverse universities by using a flexible distributed learning (DL) platform. This consortium will provide increased opportunities for DS instruction at institutions with limited infrastructure and resources, including seven minority-serving institutions. The courses will adapt the United States military's advanced DL technology to an academic setting in order to harness the power of artificial intelligence (AI) in tailoring optimal learning experiences for the specific needs of each individual student. Pervasive DL technologies help to overcome inefficiencies found at individual institutions due to small enrollments and limited faculty expertise. At least two hundred undergraduates will gain research experiences from taking the consortium's DS coursework, participating in a summer research workshop, and obtaining a DS consortium certification. To broaden this project’s overall impact on equal learning opportunities and social mobility this project will recruit students from diverse backgrounds.The project aims to implement data-driven pedagogical research on innovative DL practices across diverse universities through the use of adaptive distributed learning (ADL). The difference between DL and ADL courses is that the latter utilizes the interoperable data exchange standard of the U.S. Department of Defense to leverage the power of AI, big data, and communication technologies. ADL provides learning that can be personalized and delivered anytime and anywhere to an individual student. The adaptation of ADL technologies in an academic setting remains largely untested and would benefit greatly from an analysis of its efficacy. The consortium is organized into four organizational clusters headed by Embry-Riddle Aeronautical University (FL), the University of North Texas, and Florida A&M University. Institutions within each cluster include Bethune-Cookman University (FL), California State University at Los Angeles, Hampden-Sydney College (VA), Jackson State University (MS), Jarvis Christian College (TX), Lane College (TN), Morgan State University (MD), and Simmons University (MA). Leveraging the combined physical and intellectual resources of this alliance of diverse institutions with DL technology provides students at these institutions with the opportunity to pursue DS training on par with what would be expected in a research university setting, thereby removing barriers that may exist for these students to prepare for competition in the STEM job marketplace. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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": "12602", "attributes": { "award_id": "2230363", "title": "CEDAR: Euler potentials for field-aligned and shell currents in the ionosphere", "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": 28523, "first_name": "Evgeny", "last_name": "Romashets", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 484, "ror": "https://ror.org/008ms5s18", "name": "Lamar University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The Earth’s ionosphere is the part of the upper atmosphere that has a relatively high concentration of charged particles, largely due to solar radiation. This region, from about 50 km to 1000 km above the Earth’s surface, has complex electro-dynamic processes that have important effects in a wide range of areas from the surface magnetic field to radiation in space. The PI seeks to engage undergraduate researchers to study the field-aligned currents in the ionosphere, in which the electric current flows along the Earth’s magnetic field lines. This work will allow the students to learn how to analyze and interpret ionospheric data. The PI also aims to recruit minority students into Physics from different backgrounds via a series of workshops organized by the American Institute of Physics. The PI plans to use Euler potentials which can be derived analytically from magnetic field and applied for tracing current streamlines and charged particles trajectories. This project will use Euler potentials to reconstruct shell currents from ground-based and space-based observations, which has not been done before. This project is important for understanding ionosphere-magnetosphere-thermosphere coupling. An analytical description of electric current systems in the ionosphere will be given for the first time. The results will be useful for planning and managing low-altitude satellites and predicting GPS signal disruptions. The main expected outcome of the project is to find shell currents distribution in the ionosphere, important for reliable calculations of Joule heating. This project has three objectives: 1) To determine Euler potentials for the magnetic field in the ionosphere and in its vicinity, 2) To use the Euler potentials for calculation of charged particle motion, and 3) To find electric current streamlines using the Euler potentials. Field-aligned currents (FAC) and induced magnetic field variations with good temporal and spatial resolution can be obtained from Ionospheric data and empirical models. However, the merging between FAC and shell currents (SLC) is not determined. The application of the methods in the ionosphere will make the ionospheric currents modeling more reliable and closer to reality. This method can define each magnetic field line and track changes over time. The formation and evolution of field-aligned currents can be monitored as well. The proposed Euler potentials method can find the induced magnetic field analytically, which is a much easier and faster process that the Biot-Savart integration.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": "12603", "attributes": { "award_id": "2221421", "title": "Collaborative Research: Pacific Alliance for Low-Income Inclusion in Statistics & Data Science", "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": 28524, "first_name": "Andrew", "last_name": "Schaffner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This 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 the University of California - Santa Barbara, the University of California - Irvine, the University of Washington, California State University, East Bay, California State University, Monterey Bay, San Diego State University, and California Polytechnic State University, San Luis Obispo. The Data Revolution is generating numerous well-paid career paths, and creating a significant workforce shortage, in Statistics & Data Science. Graduate degrees are needed for many lucrative, data-rich careers, which can represent a significant barrier for low-income students. This project will provide scholarship support to approximately 115 talented, low-income undergraduate students studying statistics and data science and provide continued scholarship support for at least 65 of them over two years of graduate studies. Scholars will benefit from faculty and peer mentoring, an annual meeting that spans all seven participating schools, and support for applying to and preparing for graduate school, including a pre-grad summer bootcamp. Additional academic supports include a small-group directed reading program, shared coursework to build community within scholar cohorts, and undergraduate research opportunities.The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Additional project goals and aims include: (a) to offer a cohort-based program that supports students financially via scholarships lasting up to 3 years; (b) provide scholars with academic and co-curricular experiences designed to facilitate placement into careers in statistics and data science; and (c) offer interventions to enhance scholars’ community cultural capital. Project research will use surveys and interviews to study three main themes: (a) how counterspaces and other kinds of community develop and support scholars’ progress towards their goals; (b) how scholars’ community cultural wealth shapes and is shaped by the counterspaces and communities that develop; and (c) how students’ low-income status and other identities impact key counterspaces and communities and influence scholars’ choices and outcomes. Project evaluation will provide formative and summative feedback on all aspects of the project to support efficient progress towards goals. This project is funded by NSF’s 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 } }, { "type": "Grant", "id": "12604", "attributes": { "award_id": "2142465", "title": "Distributed Learning for Undergraduate Programs in Data Science at Diverse Universities", "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": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28525, "first_name": "Clement", "last_name": "Yedjou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 533, "ror": "https://ror.org/00c4wc133", "name": "Florida Agricultural and Mechanical University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by improving undergraduate education in data science. This project will develop and deliver ten Data Sciences (DS) courses to students from a consortium of eleven diverse universities by using a flexible distributed learning (DL) platform. This consortium will provide increased opportunities for DS instruction at institutions with limited infrastructure and resources, including seven minority-serving institutions. The courses will adapt the United States military's advanced DL technology to an academic setting in order to harness the power of artificial intelligence (AI) in tailoring optimal learning experiences for the specific needs of each individual student. Pervasive DL technologies help to overcome inefficiencies found at individual institutions due to small enrollments and limited faculty expertise. At least two hundred undergraduates will gain research experiences from taking the consortium's DS coursework, participating in a summer research workshop, and obtaining a DS consortium certification. To broaden this project’s overall impact on equal learning opportunities and social mobility this project will recruit students from diverse backgrounds.The project aims to implement data-driven pedagogical research on innovative DL practices across diverse universities through the use of adaptive distributed learning (ADL). The difference between DL and ADL courses is that the latter utilizes the interoperable data exchange standard of the U.S. Department of Defense to leverage the power of AI, big data, and communication technologies. ADL provides learning that can be personalized and delivered anytime and anywhere to an individual student. The adaptation of ADL technologies in an academic setting remains largely untested and would benefit greatly from an analysis of its efficacy. The consortium is organized into four organizational clusters headed by Embry-Riddle Aeronautical University (FL), the University of North Texas, and Florida A&M University. Institutions within each cluster include Bethune-Cookman University (FL), California State University at Los Angeles, Hampden-Sydney College (VA), Jackson State University (MS), Jarvis Christian College (TX), Lane College (TN), Morgan State University (MD), and Simmons University (MA). Leveraging the combined physical and intellectual resources of this alliance of diverse institutions with DL technology provides students at these institutions with the opportunity to pursue DS training on par with what would be expected in a research university setting, thereby removing barriers that may exist for these students to prepare for competition in the STEM job marketplace. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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": "12605", "attributes": { "award_id": "2312119", "title": "CAREER: Security and Privacy Foundations of Internet-Scale User-Centered Automation", "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": 28526, "first_name": "Earlence", "last_name": "Fernandes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "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 digital and physical resources of people, such as emails, health data, smart home, and city devices, are now accessible on the Internet. By bringing all these systems online and making them interoperable, system operators enable new functionality and drive efficiencies. The enabler of such useful interconnections is the Internet-scale automation system, whose hallmark is permitting non-programmers to create automations, thus democratizing the bridge between digital and physical resources. Unfortunately, these automation systems are not secure and do not guarantee user privacy— attackers can steal sensitive user data and manipulate resources, including physical ones, at large scale. This project pursues an integrated research and education approach to endow Internet-scale automation with the correct security and privacy foundations. The project’s novelty is leveraging the unique properties of Internet-scale automation to develop a framework for securing them that strikes different trade-offs in functionality, performance, security, and usability. The broader significance and importance of the project are empowering non-programmers to securely create automations that improve convenience, safety, and energy efficiency in a privacy-preserving fashion.To provide the correct security foundations, the project focuses on building least-privilege distributed computer systems. Specifically, the unique properties of Internet-scale automation allow the adaptation of techniques from the theory of language-based data minimization, computing on encrypted data and human-centered design. Contributions to applied cryptography and data minimization include system-level innovations to make practical use of garbled circuits and program dependency analyses. Contributions to human-centered design include empirical studies and data-driven interface designs to help users write better automation programs. Rather than finding the security architecture, the project develops a framework of security architectures that strikes different trade-off points in functionality, usability, security, privacy, and performance. The project also introduces an automation simulator that integrates research results and makes them available for experimentation to students at universities and K-12.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": "12606", "attributes": { "award_id": "2245156", "title": "Collaborative Research: CISE-MSI: DP: CNS: An Edge-Based Approach to Robust Multi-Robot Systems in Dynamic Environments", "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": 28527, "first_name": "Md Tanvir", "last_name": "Arafin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "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).Multi-robot systems consist of autonomous robots interacting in a shared environment to achieve common goals. They are widely used in real-world application domains such as transportation, disaster management, as well as warehousing and manufacturing. This project develops an efficient, robust, and secure multi-robot system, called EdgeRobot. EdgeRobot establishes an edge computing based architecture and algorithmic framework to facilitate multi-robot collaboration and coordination in dynamic environments. This work provides new model, architecture, and theory for coordinated multi-robot systems. In addition, this project builds research capacity, sustainable for training underrepresented students via the partnership of six geographically diverse minority-serving institutions in the United States: the University of Houston-Clear Lake (South), the University of Michigan Flint (North), CUNY-New York City College of Technology (Northeast), Morgan State University (East), San Francisco State University (West), and California State University Dominguez Hills (West). The cross-institutional collaboration not only boosts research capacity in all six participating institutions but also provides integrative research and education experience to their underrepresented minority students. Ultimately, this project establishes and exemplifies an effective collaboration model for training and educating underrepresented students from geographically diverse minority-serving institutions.This project consists of the following three research thrusts. First, the novel edge computing infrastructure provides optimal and location-aware computing services for collaborative robots to achieve their common goals. Besides, reinforcement learning-based algorithms solve the multi-robot scheduling and routing problems, modeled as variants of the prize-collecting traveling salesman problem. Second, in tasks requiring collaborative actions, such as cooperative target tracking, multi-agent reinforcement learning enables teams of robots to operate, learn, and adapt in dynamic and human-populated environments robustly and safely. Third, integrating modern cryptographic and security primitives secures the collaboration among edge nodes in multi-robot systems. Consequently, the interface between EdgeRobot and its human team members builds a shared autonomy model.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": 1405, "pages": 1442, "count": 14420 } } }