Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1382&sort=-keywords" }, "data": [ { "type": "Grant", "id": "9646", "attributes": { "award_id": "2100092", "title": "SBIR Phase I: A Transformational Method to Extract Polychlorinated Biphenyls (PCBs) from Building Masonry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1782, "first_name": "Rajesh", "last_name": "Mehta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-05-15", "end_date": "2022-04-30", "award_amount": 256000, "principal_investigator": { "id": 6451, "first_name": "Martha", "last_name": "Inglese", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 794, "ror": "", "name": "MARLEY ENVIRONMENTAL INC", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase 1 project is solving the legacy polychlorinated biphenyls (PCBs) problem impacting our aging schools and infrastructure. The U.S. Environmental Protection Agency (EPA) estimates as many as 55,000 schools and 800,000 government and non-government buildings may have been constructed with PCB-laden paints, caulks, mastics and adhesives before the 1979 PCBs ban. Simply removing the PCB-laden source in hopes it will eliminate the hazard has proven futile as a growing body of data is revealing PCBs from weathered caulk can leach as deep as 6-inches into adjacent porous masonry (e.g., concrete, brick, and mortar). Currently, total demolition and select removal (i.e., partial demolition) are the only EPA-approved PCB removal options and both are quickly filling up the handful of landfills willing to take it. The proposed technical innovation will transform a dormant government patent that extracts PCBs in paint, into a non-destructive treatment method that penetrates and extracts PCBs absorbed in building masonry. Such an innovation will have a direct and beneficial impact on the government agencies and school renovation commissions who cannot afford to demolish the old and rebuild new. \n\nThis SBIR Phase 1 project proposes to demonstrate the feasibility of two proprietary solvent-paste formulations at extracting PCBs from different masonry types after the source (e.g., caulk) has been removed. The solvent-paste is applied directly to the contaminated masonry surface and scraped off after a pre-determined treatment period. Once applied, the lipophilic alcohol in the solvent-paste penetrates the masonry’s open pore spaces, and solubilizes the PCB molecules it encounters along the way. The process of desorbing the PCBs from the inorganic masonry particles and into the applied paste is aided – via capillary action – by the lipophilic alcohol drawing the hydrophobic PCBs toward the paste. Technical challenges include desorbing the stickier spectrum of hydrophobic Aroclors (e.g., 1248, 1254, 1260) added to paints, caulks and adhesives in seasonally cool (< 50°F), wet weather. Since successful commercialization of an alternative PCB treatment technology requires approval from EPA in accordance with the PCB regulations (40 CFR 761), the performance of both solvent-pastes will be evaluated against the regulation’s stringent 1 ppm high occupancy cleanup criterion.\n\nThis 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": "9647", "attributes": { "award_id": "2109582", "title": "NSF Postdoctoral Fellowship in Biology FY 2021: Characterizing the role of Wolbachia in an insect vector-virus pathosystem", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Biology Postdoctoral Research" ], "program_reference_codes": [], "program_officials": [ { "id": 2342, "first_name": "John", "last_name": "Barthell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2023-08-31", "award_amount": 138000, "principal_investigator": { "id": 25465, "first_name": "Clesson", "last_name": "Higashi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1853, "ror": "", "name": "Higashi, Clesson", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2021, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the Fellow that will contribute to the area of Rules of Life in innovative ways. Sap-feeding insects harbor an array of heritable symbionts that provide important ecological benefits including nutrient acquisition or defense against natural enemies. Sap-feeding insects can also vector plant pathogens, which sometimes help their insect vector overcome plant immune responses, and further depress plant fitness. A growing number of studies indicate that insect symbionts influence transmission of insect-vectored plant viruses. Using a tractable aphid model, the Fellow seeks to investigate how the symbiont, Wolbachia, impacts the biology of the banana aphid, Pentalonia nigronervosa, and its ability to vector the plant pathogen banana bunchy top virus. The Fellow will use an interdisciplinary approach to determine how Wolbachia impacts vector ecology and banana bunchy top virus transmission dynamics. This work will broaden our understanding of the ubiquitous and multifaceted Wolbachia, but it may also provide insights into disease mitigation. The Fellow will receive training in fluorescent and electron microscopy, next-generation sequencing, comparative genomics, transcriptomics, and associated bioinformatic analyses; the project also creates opportunities to increase STEM participation by individuals from underrepresented groups.\n\nThis work seeks to understand the interface where symbionts and vectored plant pathogens may overlap and where dynamic interactions may develop with outcomes affecting multi-trophic communities. A system that provides a unique opportunity to bridge this knowledge gap is the entirely asexual banana aphid, Pentalonia, in which most individuals harbor the HFS Wolbachia, and is the sole vector of an economically important plant pathogen called banana bunchy top virus (BBTV). Despite being the most widespread and best-studied symbiont, Wolbachia’s role in aphids has rarely been investigated. The Fellow will conduct manipulative experiments using engineered aphid lines to first understand the functional role of Wolbachia in banana aphids, and to explicitly examine whether Wolbachia impacts vector competency and BBTV transmission dynamics. To link pattern with process, microscopy techniques will be used to localize Wolbachia and BBTV, and molecular assays to quantify tissue-specific abundances vital to the within-aphid ecology of this interaction. The Fellow will use ‘omics approaches to link Wolbachia’s diverse phenotypes with potential governing mechanisms; outreach to high school and elementary school students of diverse backgrounds (including from international venues) will create pathways for future scientists.\n\nThis 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": "9648", "attributes": { "award_id": "2120019", "title": "CCRI: ENS: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CCRI-CISE Cmnty Rsrch Infrstrc" ], "program_reference_codes": [], "program_officials": [ { "id": 1245, "first_name": "Deepankar", "last_name": "Medhi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 1800000, "principal_investigator": { "id": 25469, "first_name": "Thomas", "last_name": "DeFanti", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5421, "first_name": "Tajana S", "last_name": "Rosing", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25466, "first_name": "Frank", "last_name": "Wuerthwein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25467, "first_name": "Ilkay", "last_name": "Altintas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25468, "first_name": "Qi", "last_name": "Yu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Machine learning (ML) is a rapidly expanding field. Computationally intensive workflows train neural nets and then use the results in smartphones, robots, drones, self-driving vehicles, and to run the Internet of Things. Access to graphics processing units (GPUs) is provided through CHASE-CI’s Nautilus, a highly distributed but centrally managed on-demand computer cluster designed for ML and Computational Media (CM). CHASE-CI provides over 20 campuses the scaffold for adding on-premises compute cycles and fast data handling and it offers researcher-focused support and training. Using CHASE-CI’s detailed measurements of performance, researchers learn to become experts in optimization of their computational resources.\n\nCHASE-CI is a community-building effort that sustains a growing community of ML/CM researchers using a purpose-built continuously enhanced nationally distributed computing and data storage infrastructure. Researchers explore combinations of algorithms and architectures optimized with the help of graphed performance metrics. Researchers benefit from extensive shared workflows and open-source software. They use CHASE-CI’s on-line social media platform to receive and give support and share techniques. Community use of CHASE-CI informs computer architecture discussions about future national cyberinfrastructure research and instructional lab needs. CHASE-CI forms a national on-line community that is easy to join, designed for sharing code, data, and results.\n\nThe hardware, software, and socio-technical approaches developed by CHASE-CI have provided a roadmap for broader research uses and student training in ML/CM technologies. Researchers get expanded access to hundreds of GPUs for developing algorithms and software to train sensing devices and visualize results thus engaging the students who will soon join the essential workforce for the ongoing massive expansion of mobile platforms such as robots, drones, and self-driving cars. CHASE-CI impacts social diversity in computer science, broadening participation among Minority-Serving Institutions and underserved states. CHASE-CI thoroughly measures and monitors data access by applications over the regional and national R&E networks. \n\nThe repository for the project may be found at prp.ucsd.edu, to be maintained for the length of the project at a minimum. It is the anchor website containing pointers to all the research efforts that build upon the Pacific Research Platform. It contains code repositories, presentations, references like publications, presentations, and recorded lectures, and it maintains and archives an active social media channel. CHASE-CI is led by UC San Diego, partnering with investigators at UC Santa Cruz, The University of Nebraska-Lincoln, Florida Agricultural and Mechanical University, New York University, The University of Illinois at Chicago, and San Diego State University.\n\nThis 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": "9649", "attributes": { "award_id": "2113991", "title": "Productive Online Teamwork Engagement Through Intelligent Mediation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Cyberlearn & Future Learn Tech" ], "program_reference_codes": [], "program_officials": [ { "id": 1983, "first_name": "Paul", "last_name": "Tymann", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 850000, "principal_investigator": { "id": 25473, "first_name": "Alejandra", "last_name": "Magana-de-Leon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25470, "first_name": "Jennifer C", "last_name": "Richardson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25471, "first_name": "Bedrich", "last_name": "Benes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25472, "first_name": "Dominic", "last_name": "Kao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Modern work environments are becoming increasingly distributed. As a result, the ability of employees to work in a virtual environment is becoming essential for corporate success. Employers expect higher education institutions to prepare students to operate as productive members and leaders of virtual teams. While research has built compelling pedagogical frameworks to improve in-class teamwork performance, more research-based mechanisms are needed to maximize student engagement and build teamwork skills in online education environments. Focusing on large enrollment courses in higher education, this project will study the use of effective teamwork in the online classroom by (1) developing and testing technologies that promote social presence, (2) identifying pedagogies that facilitate teamwork in an online environment, and (3) promoting productive online teamwork engagement.\n\nTo promote productive online teamwork engagement, this design-based research project will develop the PECAS Mediator, an educational innovation that provides (1) AI-enabled monitoring, (2) productive and unproductive interaction detection, and (3) faculty mediation via just-in-time guidance. The high-level conjecture of this project is that monitoring and mediation, enabled by evidence-based pedagogical practices and technological innovations, will increase social presence within online teamwork sessions resulting in increased teamwork engagement. The project has two main objectives: (1) Deploy and validate the intelligent monitoring and mediation PECAS Mediator to promote social presence and collaborative learning, and (2) Investigate the effect of increased social presence and collaborative learning on teamwork engagement. The theoretical conjecture of this project is that social presence mediating processes will lead to productive engagement manifested as behavioral engagement via team effectiveness, cognitive engagement via team performance, and affective engagement via positive attitudes toward teamwork.\n\nThis 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": "9650", "attributes": { "award_id": "2106961", "title": "III: Medium: Collaborative Research: Principled Uncertainty Quantification in Deep Learning Models for Time Series Analysis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 637, "first_name": "Wei", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 675271, "principal_investigator": { "id": 25475, "first_name": "Chao", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25474, "first_name": "B Aditya", "last_name": "Prakash", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Time series data are ubiquitous in modern science and engineering. An unprecedented amount is being collected in diverse applications such as healthcare systems, the Web, cyber network monitoring, self-driving cars, and Internet-of-Things services. While deep learning has achieved enormous success in time series predictive analysis, a key bottleneck of such models is that they are ignorant about the uncertainties in their predictions. A consequence is that they can produce wildly wrong predictions without noticing---this will lead to misguided decisions, which can be catastrophic in life-critical applications. This project aims to remedy this issue and advance deep learning towards more trustworthy time series analysis. The project will enable principled deep learning models for uncertainty-aware and reliable time series regression and classification without sacrificing their predictive power. Research findings from the project will be incorporated into graduate-level classes, tutorials, and workshops to bring multiple stakeholders and domain scientists together.\n\nThe technical aims of this project are divided into three thrusts. First, the project will develop novel techniques bridging deep sequential models (e.g., recurrent networks, transformers) with Gaussian processes to quantify uncertainty in the functional space. Second, the project will explore how to learn calibrated deep sequential models and how to further decouple different sources of uncertainties to understand where a model's predictive uncertainty comes from. Third, the project will harness uncertainty to improve the reliability and efficiency of time series predictive systems. These techniques will enjoy the representation power of deep neural networks for modeling complex temporal dependencies in time-series data, while providing principled methodologies for quantifying and leveraging uncertainty for robustness and performance. The developed new models, algorithms, and techniques will be deployed in two important applications for times series analysis: 1) public health monitoring and forecasting, and 2) real-time analysis for mobile sensing time series data. The developed tools will also be open-sourced for trustworthy time series analysis that can benefit many other applications.\n\nThis 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": "9651", "attributes": { "award_id": "2104185", "title": "Women’s Political Participation in a Transitioning Democracy", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "SPRF-Broadening Participation" ], "program_reference_codes": [], "program_officials": [ { "id": 1351, "first_name": "Josie Welkom", "last_name": "Miranda", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2023-08-31", "award_amount": 143000, "principal_investigator": { "id": 25477, "first_name": "Maro", "last_name": "Youssef", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25476, "first_name": "Rhacel", "last_name": "Parrenas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1854, "ror": "", "name": "Youssef, Maro", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award was provided as part of NSF’s Social, Behavioral, and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Rhacel Parreñas at the University of Southern California (USC), this postdoctoral fellowship award supports an early career scientist investigating the role of women in politics during a democratic transition. Existing research on women’s political participation during democratic transitions generally concludes that women participate in revolutions and the early years of democratic transitions but stop shortly after. The United States,in particular, supports women political candidates and politicians, which likely improves their ability to remain politically active. Women politicians who get elected during democratic transitions deserve to be studied because they provide a model of increasing women’s political participation—not through political appointments, but rather through elections. The researcher will employ and train junior researchers to conduct policy-relevant research and communicate the research’s findings through written and oral policy briefings. \n\nResearch on women’s long-term political participation during political transitions has tended to subscribe to one of two major sets of assumptions. First, much research assumes that women do not gain political influence beyond revolutions and the initial years of democratic transition. In contrast, the literature that argues that women become influential in the long term tends to focus exclusively on post-civil war contexts. Second, there is a growing scholarly literature on coalitions. Yet, much of the literature focuses on places where political actors form coalitions under relatively stable political conditions. Third, there are scholarly and activist debates about the role and impact of assistance during democratic transitions. Findings from this research can shed light on women’s political participation during democratic transitions and on coalition work across party lines.\n\nThis 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": "9652", "attributes": { "award_id": "2142915", "title": "20th Annual Symposium of the NSF Astronomy and Astrophysics Postdoctoral Fellows", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "SPECIAL PROGRAMS IN ASTRONOMY" ], "program_reference_codes": [], "program_officials": [ { "id": 12092, "first_name": "Harshal", "last_name": "Gupta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2022-01-31", "award_amount": 38266, "principal_investigator": { "id": 25480, "first_name": "Claude-Andre", "last_name": "Faucher-Giguere", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25478, "first_name": "Sarah A", "last_name": "Wellons", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25479, "first_name": "Patrick D", "last_name": "Sheehan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 317, "ror": "https://ror.org/000e0be47", "name": "Northwestern University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This award will provide support for the attendees and invited speakers for the twentieth Annual Symposium of the NSF Astronomy and Astrophysics Postdoctoral Fellows, to be held in conjunction with the winter meeting of the American Astronomical Society in January 2022, in Salt Lake City, Utah. The purpose of the Astronomy and Astrophysics Postdoctoral Fellowships (AAPF) Program is to support integrated research and education activities at the postdoctoral level to better prepare its fellows for positions of distinction and leadership in the scientific community. The Annual Symposium provides a forum for the Fellows to discuss their research and education projects while increasing their visibility within the astronomy and astrophysics community. The Symposium represents a key component of the AAPF Program and is a very effective mechanism to facilitate the transfer of knowledge and experience that the Fellows have acquired through their postdoctoral training. \n\nAs with previous symposia in this series, the 2022 AAPF Symposium will promote interactions among astronomers with diverse research interests and backgrounds. By creating a forum in which discussions can occur across traditional research boundaries, the Symposium will provide Fellows the opportunity to gain new insights and pursue interdisciplinary collaborations. The Symposium will also provide a venue for discussing major issues that are important to early-career astronomers. In addition, the Symposium will (1) provide a forum to discuss integrated research and education activities, (2) facilitate collaborations between Fellows on both research and education, and (3) provide greater exposure for the Fellows and the AAPF Program within the astronomical community.\n\nThis 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": "9653", "attributes": { "award_id": "2121638", "title": "Collaborative Research: HCC: Small: The Market is the Interface: Online Labor Platforms and Contingent Knowledge Work", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HCC-Human-Centered Computing" ], "program_reference_codes": [], "program_officials": [ { "id": 1801, "first_name": "William", "last_name": "Bainbridge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-15", "end_date": "2024-08-31", "award_amount": 160360, "principal_investigator": { "id": 25481, "first_name": "Michael", "last_name": "Dunn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1855, "ror": "https://ror.org/04nzrzs08", "name": "Skidmore College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This longitudinal study examines the ways in which online labor platforms are reshaping work, with a focus on how they sustain their market-making roles, and how workers and employers adapt to these changes. Given the centrality of knowledge work to the U.S. economy, and the lessons of the current pandemic, findings will inform policy makers and contribute to ongoing debates on work, labor and the economy. The research will accomplish this in three ways: (1) Better understanding of platform architecture design and market adaptation, both critical to strengthening labor markets and supporting both workers and employers. (2) Deeper insights on the emerging structures of working arrangements and digitally-reliant labor strategies, for both workers and employers, to guide training, educating, policy-formation, and worksite support for an emerging form of future work. (3) Specific analysis of each role that online market-making platforms can play in redressing, exacerbating, or transforming known issues with differential treatment of women workers and workers from under-represented populations. The research will significantly advance current understanding about the ways that online labor market interfaces both replicate and address known differences in access to labor due, in part, to the worker's gender, race, and ethnicity. \n\nData collection focuses on one type of contingent knowledge work: online freelancing conducted through online labor platforms that support human-computer interactions and enable a two-sided labor market. Freelancers (who sell their services) and employers (who seek the services of sellers) interact through the different interfaces provided by the market-making platforms, rendering this a negotiating space. That is, a study of online labor is also a study of market making, platform architecture, and humans and computer-based systems interacting. Building from current work and the relevant literature, this study pursues three primary research questions: (1) How does an online labor platform sustain its market-making role? (2) How do freelancers adapt to changes on a platform? (3) How do employers adapt to changes on a platform? Findings will provide a transformative lens into the ways in which labor markets are creating a new form of digitally-reliant labor infrastructure.\n\nThis 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": "9654", "attributes": { "award_id": "2121624", "title": "Collaborative Research: HCC: Small: The Market is the Interface: Online Labor Platforms and Contingent Knowledge Work", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HCC-Human-Centered Computing" ], "program_reference_codes": [], "program_officials": [ { "id": 1801, "first_name": "William", "last_name": "Bainbridge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-15", "end_date": "2024-08-31", "award_amount": 339362, "principal_investigator": { "id": 25482, "first_name": "Steven", "last_name": "Sawyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 579, "ror": "https://ror.org/025r5qe02", "name": "Syracuse University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This longitudinal study examines the ways in which online labor platforms are reshaping work, with a focus on how they sustain their market-making roles, and how workers and employers adapt to these changes. Given the centrality of knowledge work to the U.S. economy, and the lessons of the current pandemic, findings will inform policy makers and contribute to ongoing debates on work, labor and the economy. The research will accomplish this in three ways: (1) Better understanding of platform architecture design and market adaptation, both critical to strengthening labor markets and supporting both workers and employers. (2) Deeper insights on the emerging structures of working arrangements and digitally-reliant labor strategies, for both workers and employers, to guide training, educating, policy-formation, and worksite support for an emerging form of future work. (3) Specific analysis of each role that online market-making platforms can play in redressing, exacerbating, or transforming known issues with differential treatment of women workers and workers from under-represented populations. The research will significantly advance current understanding about the ways that online labor market interfaces both replicate and address known differences in access to labor due, in part, to the worker's gender, race, and ethnicity. \n\nData collection focuses on one type of contingent knowledge work: online freelancing conducted through online labor platforms that support human-computer interactions and enable a two-sided labor market. Freelancers (who sell their services) and employers (who seek the services of sellers) interact through the different interfaces provided by the market-making platforms, rendering this a negotiating space. That is, a study of online labor is also a study of market making, platform architecture, and humans and computer-based systems interacting. Building from current work and the relevant literature, this study pursues three primary research questions: (1) How does an online labor platform sustain its market-making role? (2) How do freelancers adapt to changes on a platform? (3) How do employers adapt to changes on a platform? Findings will provide a transformative lens into the ways in which labor markets are creating a new form of digitally-reliant labor infrastructure.\n\nThis 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": "9655", "attributes": { "award_id": "2203207", "title": "Statistical Modelling and Inference for Next-Generation Functional Data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "STATISTICS" ], "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": "2021-10-01", "end_date": "2023-07-31", "award_amount": 124999, "principal_investigator": { "id": 933, "first_name": "Lily", "last_name": "Wang", "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": "With the rapid growth of modern technology, many large-scale imaging studies have been or are being conducted to collect massive datasets with large volumes of imaging data, thus boosting the investigation of \"next-generation functional data\". These enormous collections of imaging data contain interesting information and valuable knowledge, which has raised the demand for further advancement in functional data analytic approaches. Although functional data analysis (FDA) has gained widespread popularity in recent years, enhancing the capability of next-generation FDA remains a long-standing challenge. This research targets integrating state-of-the-art statistical modeling devices with modern computational and inferential techniques to develop a set of flexible and intelligent statistical tools to enable learning and discovery from next-generation functional data. The efficacy of the tools developed in this research will be tested by neuroimaging studies. The proposed methods and theory are also applicable to a broader range of fields that require modeling and analysis of images and other complex data types collected over space and/or time, such as geography, environmental science and remote sensing studies. The graduate student support will be used for day-to-day research activities, including parts of the theory/methodology developments and data analysis. \n\nThis research will enrich the methods for dealing with functional data observed from complex data objects (high-dimensional, correlated images or shapes), which commonly arise in imaging studies, such as, health/medical imaging or remote sensing imaging. The PI aims to address some challenging research problems in analyzing next-generation functional data by: (1) innovating a statistically sound framework to extract useful information from large-scale longitudinal imaging studies; (2) developing flexible and intelligent statistical models to delineate the association between massive imaging data and covariates of interest and to characterize and visualize the spatial variability of the imaging data; and (3) developing efficient, scalable algorithms with high-performance statistical software packages to meet the challenges posed by dynamic imaging studies. In particular, the proposed research involves four projects. Project 1 provides a unifying approach to characterize the varying association between imaging responses with a set of explanatory variables. Project 2 focuses on the interface between high-dimensional and next-generation functional data to address several fundamental bottlenecks in large-scale imaging genetics studies. Projects 3 and 4 deal with longitudinal/dynamic imaging studies, and a comprehensive functional regression framework to analyze repeated functional responses from these studies will be developed.\n\nThis 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": 1383, "pages": 1424, "count": 14236 } } }