Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=4&sort=principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1397&sort=principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=5&sort=principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=principal_investigator" }, "data": [ { "type": "Grant", "id": "343", "attributes": { "award_id": "2208821", "title": "WiGRAPH: Women in Graphics Research", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 612, "first_name": "Ephraim", "last_name": "Glinert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2023-03-31", "award_amount": 49600, "principal_investigator": { "id": 613, "first_name": "Adriana", "last_name": "Schulz", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "WiGRAPH’s mission is to increase the number of women pursuing cutting-edge research in computer graphics, which despite some progress in recent years, remains distressingly low. To this end, the group creates online resources for women in the field through the wigraph.org website and social media (e.g., online discussion forums). In addition, it hosts events during the main computer graphics conferences that provide environments where women researchers can interact with one another and seek role models, mentorship, and encouragement. In 2022 the plan is to host three such networking events at the premier graphics conferences: SIGGRAPH, SIGGRAPH ASIA, and the Symposium on Geometry Processing (SGP), and also to launch a new Rising Stars in Computer Graphics program whose goal is to provide mentorship at a time when students have sufficient research maturity, but are still making decisions about future career directions. WiGRAPH activities are always centered around issues relating to research, such as sharing advice about how to pick research topics, pursue research questions, and navigate the industry/academic markets. Statistics are gathered from event registrations and participant testimonials to help generate clear demographic data about the population being served by and to evaluate the impact of sponsored events.Although all the organization work is done with volunteered time, the events program comes at a substantial cost. Due to COVID-19, all 2020 and 2021 events were held online and therefore required only minimal funding, but moving forward it is estimated that the basic program will cost roughly $49,600 for 2022, as described in detail in the proposal, and the organizers hope to augment the program, for example by increasing the number of participants, if they are able to raise additional funds with industry sponsorship. HCC PDs discussed this proposal and agreed that the goals are both laudable and timely, and that the planned activities are likely to have broad impact.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": "344", "attributes": { "award_id": "2111898", "title": "SBIR Phase I: A mobile, device-based, screening tool for assessing K-6 students’ cognitive and motor skills via machine learning handwriting analysis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 614, "first_name": "Diane", "last_name": "Hickey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2023-03-31", "award_amount": 255993, "principal_investigator": { "id": 615, "first_name": "Renee", "last_name": "Cassuto", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 162, "ror": "", "name": "OT APP DESIGN LLC", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 162, "ror": "", "name": "OT APP DESIGN LLC", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase 1 project is to improve the assessment of cognitive and motor skills in K-6 students. More specifically, this project will develop and perform feasibility testing of a novel, objective, and machine learning-driven approach to analyzing student handwriting proficiency, a measure of cognitive and motor skills. Through the use of a smart device application, end-users (teachers, aides, and parents) will be able to take a photo of a student's handwriting and receive immediate results regarding proficiency, handwriting error types, and targeted intervention suggestions. Given the myriad of visual motor, fine motor, and higher-order cognitive skills needed to generate a handwriting sample and the fact that up to 30% of students have difficulties, there is a need for new and better detection schemes. The identification of cognitive and motor skill deficiencies is becoming especially important with the increased use of virtual learning environments due to the COVID-19 crisis. Students are interfacing more with computers, and teachers have decreased access to handwriting assignments.This Small Business Innovation Research (SBIR) Phase 1 project focuses on developing machine learning (ML) algorithms to generate highly accurate, rapid, and objective predictions of handwriting proficiency. These algorithms seek to predict the handwriting error sub-type. ML analysis of handwriting images has never been done before. Through the use of data annotation schemes, highly sensitive and grade-specific algorithms will be created and accessed by a smart device application following the acquisition of a single photo of a single handwritten sentence. This technology is envisioned as a universal screening tool to be used at the beginning of each school year to identify students with subpar handwriting proficiency. The real-time analysis of handwriting proficiency will allow for earlier identification and earlier interventions to improve student outcomes and deliver cost savings to school districts.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": "345", "attributes": { "award_id": "2149108", "title": "The Seasonal Behavioral Ecology of Respiratory Disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 616, "first_name": "Jeffrey", "last_name": "Mantz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-12-01", "end_date": "2025-11-30", "award_amount": 384308, "principal_investigator": { "id": 617, "first_name": "Kathrine E", "last_name": "Starkweather", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 163, "ror": "https://ror.org/02mpq6x41", "name": "University of Illinois at Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 163, "ror": "https://ror.org/02mpq6x41", "name": "University of Illinois at Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Respiratory illnesses, like those caused by viruses like COVID-19, Influenza, and RSV, are among the leading causes of death for American citizens, especially for those in older and younger age groups, and for immunocompromised individuals. Global spread of these viruses continues to pose a threat in the United States; therefore, it is important to understand how they are transmitted in different cultural settings. In addition to the potential public health benefits of this study, it facilitates scientific training of a diverse group of graduate and undergraduate students. Public dissemination of findings will also inform community members on the roles of the household and extended family members in transmitting disease, and measures to prevent transmission of respiratory viruses among family and friends.This study is designed to assess how social behavior and environmental conditions interact to affect the transmission of common respiratory viruses and longer-term health impacts of illness in a tropical setting. To do so, the research team is collecting year-round, community-wide data on social networks and symptoms of respiratory illnesses, seasonal biological markers of respiratory infection, and seasonal anthropometric measurements, including height, weight, and upper arm circumference. In-depth interviews are being conducted to gain an understanding of how people think about illness risk. This study tests the theory that humans use behavior to adapt to environmental circumstances, and that they do so in a way that maximizes benefits and minimizes risks associated with the behavior. This may help to inform future public health policy on how best to minimize transmission of respiratory viruses.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": "346", "attributes": { "award_id": "2216879", "title": "RAPID: STEM faculty support to address impacts from COVID-19 on Tribal Colleges and Universities Program institutions", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 618, "first_name": "Lura", "last_name": "Chase", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2023-03-31", "award_amount": 134196, "principal_investigator": { "id": 619, "first_name": "Mandy", "last_name": "Schram", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 164, "ror": "", "name": "Red Lake Nation College", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 164, "ror": "", "name": "Red Lake Nation College", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "A goal of the Tribal Colleges and Universities Program (TCUP) is to increase the science, technology, engineering and mathematics (STEM) instructional and research capacities of specific institutions of higher education that serve the Nation's indigenous students. Expanding the STEM curricular offerings at these institutions expands the opportunities of their students to pursue challenging, rewarding careers in STEM fields, provides for research studies in areas that may be culturally significant, and encourages a community and generational appreciation for science and mathematics education, and sustainability of capacity gains is significantly enhanced by retaining the talent of credentialed STEM faculty. This project aligns directly with that goal.The coronavirus pandemic of 2020-2021 caused major disruptions to institutions of higher education. However, for tribal colleges and universities, whose core operating funds are directly aligned with student enrollment, drops in enrollment equate to loss of funding. To mitigate against detrimental effects on STEM instructional capacity, this award will support the position of one full-time STEM faculty member, as well as other resources to maintain Red Lake Nation College’s STEM program as it recovers from the impact of the pandemic.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": "347", "attributes": { "award_id": "2213746", "title": "RAPID: STEM faculty support to address impacts from COVID-19 on Tribal Colleges and Universities Program institutions", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 620, "first_name": "Lura", "last_name": "Chase", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2023-02-28", "award_amount": 200000, "principal_investigator": { "id": 621, "first_name": "Karen M", "last_name": "Colbert", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 165, "ror": "https://ror.org/05mchsq43", "name": "Keweenaw Bay Ojibwa Community College", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 165, "ror": "https://ror.org/05mchsq43", "name": "Keweenaw Bay Ojibwa Community College", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "A goal of the Tribal Colleges and Universities Program (TCUP) is to increase the science, technology, engineering and mathematics (STEM) instructional and research capacities of specific institutions of higher education that serve the Nation's indigenous students. Expanding the STEM curricular offerings at these institutions expands the opportunities of their students to pursue challenging, rewarding careers in STEM fields, provides for research studies in areas that may be culturally significant, and encourages a community and generational appreciation for science and mathematics education, and sustainability of capacity gains is significantly enhanced by retaining the talent of credentialed STEM faculty. This project aligns directly with that goal.The coronavirus pandemic of 2020-2021 caused major disruptions to institutions of higher education. However, for tribal colleges and universities, whose core operating funds are directly aligned with student enrollment, drops in enrollment equate to loss of funding. To mitigate against detrimental effects on STEM instructional capacity, this award will support the position of one full-time STEM faculty member, as well as other resources to maintain Keweenaw Bay Ojibway Community College’s STEM program as it recovers from the impact of the pandemic.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": "348", "attributes": { "award_id": "2151859", "title": "Collaborative Research: Unraveling Structural and Mechanistic Aspects of RNA Viral Frameshifting Elements by Graph Theory and Molecular Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 622, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2026-04-30", "award_amount": 88491, "principal_investigator": { "id": 623, "first_name": "Alain", "last_name": "Laederach", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 166, "ror": "https://ror.org/0130frc33", "name": "University of North Carolina at Chapel Hill", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 166, "ror": "https://ror.org/0130frc33", "name": "University of North Carolina at Chapel Hill", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Programmed ribosomal frameshifting is indispensable to many viruses, including HIV and SARS-associated coronaviruses, to translate overlapping reading frames on the mRNA so that essential viral proteins can be produced. Because modulation of frameshifting has been shown to dramatically influence viral viability, the RNA frameshifting element (FSE) has been an attractive anti-viral drug target. However, the complex aspects of frameshifting must be understood before therapeutic strategies can succeed. Following a 2020 NSF RAPID award, the Schlick mathematics/computational biology lab, in collaboration with the Laederach experimental RNA group, will combine graph theory applications to RNA (RAG: RNA-As-Graphs) with biophysical studies and biomolecular modeling/simulation to unravel structures and mechanisms of the RNA FSE of SARS-CoV-2 and related viruses. The collaborative research program will be the basis for interdisciplinary training of students and postdoctoral fellows, including women and minorities, in mathematics, computer science, biology, physics, chemistry, and engineering, through computer program development, data analysis, and biological interpretations. Students and postdocs will learn to analyze, process, and visualize biological data; devise and validate models; develop simulation algorithms and coarse-grained models; and collect and interpret structural/functional patterns to yield new mathematical and biophysical relationships. The project will describe conformations and structural transitions of the FSE of SARS-CoV-2 from phylogenetic and biophysical viewpoints by exploiting global representation of mathematical RNA graphs. Specifically, the researchers will gain insight into the evolutionary path of the FSE of coronaviruses by computing and validating experimentally RNA secondary-structure conformational landscapes of the FSE of SARS-CoV-2 relatives; probe frameshifting mechanisms by determining the SARS-CoV-2 FSE's transition pathway; and identify and test experimentally structure-altering mutations to transform the FSE into complex intertwined motifs by RAG inverse folding and genetic algorithms to hamper frameshifting. This unique approach applied to frameshifting elements in coronaviruses including SARS-CoV-2 using novel mathematical graph-theory tools and biophysical models will yield crucial insights into the structure, mechanisms, and evolutionary trends in related viruses to explain the relationship between viral structure and frameshifting efficiency/viral viability. By looking at structure from a global graph theory point of view, patterns can be discerned and related more easily than sequence or atomic-based models. The determined structures, mechanisms, and structure-altering mutations define gene therapy and anti-viral targets for therapeutic interventions.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": "349", "attributes": { "award_id": "2151777", "title": "Collaborative Research: Unraveling Structural and Mechanistic Aspects of RNA Viral Frameshifting Elements by Graph Theory and Molecular Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 624, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2026-04-30", "award_amount": 131000, "principal_investigator": { "id": 625, "first_name": "Tamar", "last_name": "Schlick", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Programmed ribosomal frameshifting is indispensable to many viruses, including HIV and SARS-associated coronaviruses, to translate overlapping reading frames on the mRNA so that essential viral proteins can be produced. Because modulation of frameshifting has been shown to dramatically influence viral viability, the RNA frameshifting element (FSE) has been an attractive anti-viral drug target. However, the complex aspects of frameshifting must be understood before therapeutic strategies can succeed. Following a 2020 NSF RAPID award, the Schlick mathematics/computational biology lab, in collaboration with the Laederach experimental RNA group, will combine graph theory applications to RNA (RAG: RNA-As-Graphs) with biophysical studies and biomolecular modeling/simulation to unravel structures and mechanisms of the RNA FSE of SARS-CoV-2 and related viruses. The collaborative research program will be the basis for interdisciplinary training of students and postdoctoral fellows, including women and minorities, in mathematics, computer science, biology, physics, chemistry, and engineering, through computer program development, data analysis, and biological interpretations. Students and postdocs will learn to analyze, process, and visualize biological data; devise and validate models; develop simulation algorithms and coarse-grained models; and collect and interpret structural/functional patterns to yield new mathematical and biophysical relationships.The project will describe conformations and structural transitions of the FSE of SARS-CoV-2 from phylogenetic and biophysical viewpoints by exploiting global representation of mathematical RNA graphs. Specifically, the researchers will gain insight into the evolutionary path of the FSE of coronaviruses by computing and validating experimentally RNA secondary-structure conformational landscapes of the FSE of SARS-CoV-2 relatives; probe frameshifting mechanisms by determining the SARS-CoV-2 FSE's transition pathway; and identify and test experimentally structure-altering mutations to transform the FSE into complex intertwined motifs by RAG inverse folding and genetic algorithms to hamper frameshifting. This unique approach applied to frameshifting elements in coronaviruses including SARS-CoV-2 using novel mathematical graph-theory tools and biophysical models will yield crucial insights into the structure, mechanisms, and evolutionary trends in related viruses to explain the relationship between viral structure and frameshifting efficiency/viral viability. By looking at structure from a global graph theory point of view, patterns can be discerned and related more easily than sequence or atomic-based models. The determined structures, mechanisms, and structure-altering mutations define gene therapy and anti-viral targets for therapeutic interventions.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": "350", "attributes": { "award_id": "2152774", "title": "Accelerating Bayesian Dimension Reduction for Dynamic Network Data with Many Observations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 626, "first_name": "Yulia", "last_name": "Gel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2022-07-01", "end_date": "2025-06-30", "award_amount": 300000, "principal_investigator": { "id": 627, "first_name": "Andrew", "last_name": "Holbrook", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Global viral epidemics produce vast amounts of high-dimensional spatiotemporal data. Scientists, businesses, governments and independent organizations want to learn from this data so they can understand basic biological mechanisms, invest capital, allocate aid and design coherent policy in a changing world. Analyzing spatial associations within viral contagion is, unsurprisingly, an area of immense scientific interest, but the task requires accounting for the dynamic and multiscale transportation networks that shape the global economy. This project seeks to advance knowledge of statistical inference from stochastic process models in the context of massive amounts of dynamic and network-indexed data. The proposed research ideas will avoid costly direct representations of network structure and instead use Bayesian dimension reduction to probabilistically map network dynamics to a continuous domain. The project combines theoretical and methodological developments in scalable Bayesian dimension reduction; develops efficient algorithms into open-source, high performance computing (HPC) software; and applies them to the high-impact analysis of viruses including, but not limited to, SARS-CoV-2. The project will emphasize the combination of rigorous statistical methodology with parallel computing techniques available to any scientist with moderate resources.The project will combine theory, methods and applications in advancing knowledge of statistical inference for network-indexed processes. Bayesian multidimensional scaling (BMDS) stands as an established tool for probabilistic dimension reduction of network data but the method's quadratic computational complexity prohibits big data application. The project will extend BMDS to the analysis of millions of data points using a multipronged approach. From a theoretical standpoint, the investigators will show that the classical BMDS model is strictly equivalent to a modified BMDS model with sparse couplings between observations. This 'free lunch' result will amount to a linear reduction in the computational complexity of the classical algorithm, but its use will require an upper bound on the rank of the traditional BMDS distance matrix. A jointly methodological and theoretical investigation will develop a cutting-edge rank estimation procedure for Euclidean distance matrices (EDM) and derive non-asymptotic and asymptotic bounds for the rank estimation error and its impact on the modified BMDS posterior. Bayesian inference with the developed sparse BMDS (S-BMDS) will amount to simulating a massive N-body problem with sparse pairwise couplings. A primary methodological investigation will develop fast parallel algorithms for computing (1) the S-BMDS likelihood and gradient, and (2) the EDM rank in ways that efficiently use multi-core and vectorized central processing units (CPU) and multiple graphics processing units (GPU). The investigators will then allow trends in Google mobility data to inform effective distances between viruses and use our developed machinery to model the spread of, e.g., SARS-CoV-2 through global mobility space. The project also includes an expansive plan for educational, outreach and mentoring activities and will actively disseminate the research findings in a form of open-source HPC software.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": "351", "attributes": { "award_id": "2141327", "title": "Boston University Conference on Language Development, Post-COVID", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 628, "first_name": "Tyler", "last_name": "Kendall", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2026-01-31", "award_amount": 185365, "principal_investigator": { "id": 631, "first_name": "Charles B", "last_name": "Chang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 168, "ror": "", "name": "Trustees of Boston University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 629, "first_name": "Paul A", "last_name": "Hagstrom", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 630, "first_name": "Amy M", "last_name": "Lieberman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 168, "ror": "", "name": "Trustees of Boston University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Boston University Conference on Language Development (BUCLD)is an internationally recognized meeting of researchers in language acquisition and development, including first and second language acquisition, language disorders, bilingualism, and literacy development. BUCLD promotes the progress of science by providing a major venue for dissemination of scholarly findings, for initiation and development of collaborations, and for professional networking. The conference consists of approximately 190 presentations selected through peer review, as well as invited keynote and plenary speakers, a symposium on a topic of current interest, a research funding workshop, a professional development workshop for students and early-career scholars, mentoring opportunities, and a publisher exhibit. Attendees number over 500 researchers from around the world, from undergraduates through senior faculty. Full ASL interpreting coverage is provided throughout the conference, and conference proceedings are published soon after the conference. This award supports accessibility, diversity, and inclusion at BUCLD by subsidizing travel expenses for students, including students from diverse racial/ethnic backgrounds, students with disabilities, and first-generation college students. In addition, funding supports the effort of the student conference organizers, who are graduate students in Linguistics and allied fields. Support for student travel enables student contributions—regularly among the highest-rated abstract submissions—to be presented at the conference without undue hardship. Attendance at BUCLD increases the exposure of students to the top research and researchers in the field in a friendly and interactive environment. Furthermore, support for student organizers allows BUCLD to play an important role in training students as future professionals. Finally, funding supports development of a sustainable and flexible model for BUCLD that enables continuity of the conference in future years and navigates the challenges and opportunities of scientific conferences in a post-COVID world.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": "352", "attributes": { "award_id": "2212430", "title": "I-Corps: AI-assisted Job Search in the Aftermath of the Covid-19 Crisis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 632, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2022-09-30", "award_amount": 50000, "principal_investigator": { "id": 633, "first_name": "Anant", "last_name": "Nyshadham", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "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": "The broader impact/commercial potential of this I-Corps project is the development of a technology that will help customer-facing businesses hire from a pool of candidates that have no prior experience in the sector. The proposed technology may be used by hiring managers in mid- to large-scale employers of entry-level service workers. It is anticipated that the beachhead market will be retail. The key insights from preliminary customer discovery were that in large stores with >10k employees, the turnover in entry level positions is very high (e.g., 76% in retail in 2019) and each entry-level vacancy attracts a high volume of applications (e.g., up to 1,000 applications per position). Applicants for these vacancies are often indistinguishable given limited technical skills and previous experience. Therefore, current screening process for entry-level positions relies heavily on arbitrary filtering heuristics, individual judgement, and interviews, and, as a result, can be particularly prone to discrimination. In addition, employers aim to hire candidates with particular soft skills (e.g., conscientiousness, attention to detail, intrinsic motivation, communication), but find it difficult to objectively measure them. In addition, the cost of replacing a worker who leaves, excluding lost productivity, is high (typically 1.5 to 2.5 times the worker’s annual salary). By focusing on psychometric and simulated tasks, the proposed technology: a) reduces the risk of systemic discrimination while hiring; b) allows employers to cheaply, quickly and fairly identify best-suited candidates from a pool of indistinguishable candidates; c) have the ability to match workers to best-suited occupations by documenting the transferability of skills across occupations and sectors. The hypothesis is that the technology may reduce screening and hiring costs by more than 60% per head hired, in addition to finding employees that attrit at a lower rate.This I-Corps project is based on the development of machine learning classification algorithms that use psychometric profiles and performance on simulated tasks to identify best-suited candidates for entry-level customer facing occupations. These tasks have been designed in partnership with employers in each industry to mirror actual work. To select the optimal algorithm for each task, the proposed technology uses a cost function that takes into account the losses to the employer from wrongfully rejecting qualified individuals as well as from interviewing unqualified applicants. Additionally, the proposed technology uses labor market parameters in balancing false positive and false negative predictions in selecting and calibrating the algorithms, which pushes even the frontier in this space, and is beyond the capabilities of any practical solutions in the market today.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": 4, "pages": 1397, "count": 13961 } } }