Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-id
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Susan", "last_name": "Jurow", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 172, "ror": "", "name": "University of Colorado at Boulder", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1107, "first_name": "Britte H", "last_name": "Cheng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 172, "ror": "", "name": "University of Colorado at Boulder", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The United States has historically been the global leader in the interdisciplinary field of the learning sciences, which involves the study of teaching and learning in contexts ranging from children learning math in school, to adults learning highly technical jobs in the workforce. Research in the learning sciences draws from diverse methodologies and domains, including the fields of psychology, sociology, linguistics, design studies, cognitive science, and computer science. The preeminent conference in this field is the International Society of Learning Sciences Conference (ICLS). At this conference the latest research is presented and practitioners learn the state of the art techniques that support the research and design of learning interventions, integrated with advanced technologies. This project supports a Doctoral Consortium and Early Career Workshop for approximately 10-20 attendees, providing opportunities for the participants to interact with international researchers and practitioners. The conference and related activities will address important challenges associated with the transformation of education and broadening of participation in the digital age. The conference will be held virtually this year due to Covid-19. Building upon prior ICLS conferences, several activities and mentoring for participants will be included- both before, during and after the conference. Prior to the conference, doctoral consortium participants will interact with assigned mentors to discuss research interests and prepare a research presentation covering dissertation progress. During the workshop, they will share this presentation in an interactive format, facilitated by the mentors. After the conference, activities will include online discussions to extend the workshop experience. Activities for the Early Career Workshop are similar, but with a focus on community building and professional development, including mentoring regarding tenure and career path. All participants will interact with a career panel, journal editors, and with each other in a virtual reception. The activities are important to build capacity and support new researchers in the interdisciplinary areas of the computer and learning sciences, supporting participants who then go on to mentor graduate students and other junior researchers.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": "532", "attributes": { "award_id": "2006808", "title": "Semiparametric Methods for Data Assimilation and Uncertainty Quantification", "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": 1104, "first_name": "Eun Heui", "last_name": "Kim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 233747, "principal_investigator": { "id": 1105, "first_name": "Tyrus H", "last_name": "Berry", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "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": "There is a growing demand in many scientific disciplines for efficient tools to automatically learn models and make predictions from limited noisy observations. For these predictions to be actionable, they must also have quantifiable uncertainty, and be robust to model misspecification. This is particularly relevant in light of events such as the COVID-19 pandemic, where models have to be constantly adapted to include new phenomena such as unreported and asymptomatic cases and constantly evolving social distancing rules and compliance. Other applications include large complex systems such as weather forecasting and social network dynamics where first-principles models are powerful but have difficulty capturing the full range of phenomena involved. The semiparametric framework will help address the growing problem of un-modeled phenomena by allowing existing models to be automatically merged with model-free methods that leverage data to learn a correction to the model in order to match the observed data. The new tools will allow application to a class of high dimensional problems with spatial structure, such as geosystems problems, social networks, and global disease dynamics. Beyond improving forecasting, the semiparametric approach will include accurate uncertainty quantification, which is critical in these application domains. The investigator will train a graduate student and undergraduate students who will be able to carry this research forward, as well as developing and disseminating this key expertise. These students will learn to apply both state-of-the-art and the newly developed methods which will prepare them for future work in applied and computational mathematics.The investigator will develop semiparametric modeling techniques that optimally leverage the strengths of parametric (model based) and nonparametric (model-free or data-driven) methods. Specifically, the semiparametric framework allows the flexible nonparametric models to fill in the gaps and correct the low-dimensional model error in a parametric model. The framework employs an ensemble of states in the parametric model to represent the uncertainty in a forecast or state estimate, while a full probability distribution is estimated for the nonparametric model. At each filtering or forecasting step, the ensemble is updated by sampling individual corrections from the model error distribution estimated by the nonparametric model. These sampled corrections will automatically correct biases in the model and inflate the uncertainty when necessary in order to match reality. The evolution of the nonparametric model will typically need to be conditional to the high-dimensional state of the parametric model, which current methods to do not allow. In other words, information must flow in both directions: the nonparametric model corrects the parametric model, but is also informed by the current state of the parametric model. In order to overcome this crucial challenge, supervised dimensionality reduction techniques will be combined with a novel method of learning mappings between non-diffeomorphic spaces. This will allow a Bayesian update of the nonparametric state estimate based on the learned projection of the parametric state. The research includes a novel higher order unscented ensemble forecast that will form the basis for a higher order Kalman filter. These advances will make the best use of available computation resources, since the higher order ensemble forecasting and filtering methods can scale up from small to large ensembles as resources allow. The higher order methods will improve accuracy and uncertainty quantification by estimating higher order moments of the state estimate and the forecast. For the ensemble forecast, a novel multivariate quadrature method will be applied that uses rank-1 tensor decompositions of the higher moments as quadrature nodes. For the Kalman update, higher order equations will be used based on a maximum entropy closure of the moment equations derived from the Kushner equation (which fully describes the true solution). The advances will effectively use data to learn a model-free correction to a parametric model, simultaneously alleviating model error and the curse-of-dimensionality.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": "531", "attributes": { "award_id": "2037827", "title": "Developing online teaching tools for field ecology and data science through an EREN-NEON partnership", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1098, "first_name": "Charlotte", "last_name": "Roehm", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-06-30", "award_amount": 86735, "principal_investigator": { "id": 1103, "first_name": "Laurel", "last_name": "Anderson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 285, "ror": "https://ror.org/02qj9qr34", "name": "Ohio Wesleyan University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1099, "first_name": "Jason", "last_name": "Kilgore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1100, "first_name": "Danielle", "last_name": "Garneau", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1101, "first_name": "Kaitlin S", "last_name": "Whitney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1102, "first_name": "Allison T", "last_name": "Parker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 285, "ror": "https://ror.org/02qj9qr34", "name": "Ohio Wesleyan University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "This award to Ohio Wesleyan University facilitates adaptation of ongoing and new projects by the Ecological Research as Education Network (EREN) into online learning modules. The modules will integrate data generated locally by students and instructors with NEON data, providing the foundation for the learning experiences. Field-based ecological research experiences across diverse locations will be enabled, mitigating the severe impact of the COVID-19 pandemic on undergraduate education, specifically ecological field research experiences. The modules will be widely disseminated to increase their accessibility, use and impacts, including building students’ skills and competencies in data management and analysis. The project also presents a strategy to engage with early career faculty and faculty from underrepresented groups through the relationships that both EREN and NEON have built, supported by the wide distribution to increase use of the modules.The award leverages the relationship that EREN has developed with undergraduate institutions that serve as primary conduits for preparing undergraduate students for STEM careers. Thus, the training modules will facilitate the preparation of the next generation of research scientist, promoting the development of the intellectual foundation, and the technical and analytical skills needed for success. The project takes advantage of the networks and collaborations developed through EREN and is augmented through partnering with NEON scientists. EREN’s experience enabling collaborative field ecology projects, distributed across multiple sites and targeting mostly undergraduate institutions is complemented by NEON’s high-quality, standardized data from 81 field sites across the United States. This partnership between EREN and NEON will enable the development of novel collaborative research projects driven by NEON data. The impact of the modules will be enhanced through wide distribution, use in webinar training sessions, and the publishing of written materials on the EREN and NEON websites. Plans to strengthen the interactions with future, in-person workshops will help to consolidate and sustain the collaborations.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": "530", "attributes": { "award_id": "2035541", "title": "Understanding Role-differentiated Privacy During COVID-19 Test-Trace-Isolate", "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": 1096, "first_name": "Andruid", "last_name": "Kerne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2022-06-30", "award_amount": 199948, "principal_investigator": { "id": 1097, "first_name": "Lilly C", "last_name": "Irani", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "In response to the COVID-19 pandemic, colleges and universities are running test-trace-isolate programs to safely reopen campuses. Individuals’ health gets documented with temperature checks, surveys about recent contacts, and COVID-19 tests. This requires people to feel safe sharing information about themselves. However, people may not feel safe sharing their information in workplaces and educational settings. Individuals occupying different social roles—such as temporary faculty, graduate research assistants, undergraduate students, and staff—are likely to have different sensitivities, needs, healthcare access, use of social services, and job security. Who will their information be shared with? How long will it be kept? How is this communicated? How these concerns are addressed will shape how people are willing to participate in health monitoring, which has implications for the success and safety of reopening programs, and further, for American values of freedom and privacy. This project examines how individuals occupying different social roles experience their privacy and interact with health monitoring systems. Researchers will produce recommendations for technology and program design strategies that take into consideration heterogeneous needs, vulnerabilities, and privacy expectations. The recommendations will be broadly disseminated through a toolkit and workshops.This project investigates how to meet people’s varied privacy needs—so they will feel safe participating in test-trace-isolate programs—by addressing two key research questions: (1) What different social roles impact privacy experiences? (2) What do differences in privacy experiences indicate about the design requirements (e.g. technology, interaction, and communication) of programs and systems? The project will contribute an alternative to universalizing analyses of privacy. Instead, it will develop a role-differentiated framework for understanding privacy needs and values, affected by people’s roles at home, in the workplace, and in society. The research questions will be investigated through a qualitative research methodology —involving workshops and interviews with diverse campus stakeholders—at a single, large college campus: the University of California, San Diego. UCSD’s health monitoring program has already brought many students and workers back to campus. It affects how people get health care, work, and study. Results of this research will include a new theory of privacy that respects people’s needs according to social roles.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": "529", "attributes": { "award_id": "2034642", "title": "HI-TEC 2020: Transforming to a Virtual Event and Dissemination Opportunity", "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": 1094, "first_name": "Virginia", "last_name": "Carter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-07-31", "award_amount": 48567, "principal_investigator": { "id": 1095, "first_name": "Mark L", "last_name": "Whitney", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 284, "ror": "", "name": "CORD", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 284, "ror": "", "name": "CORD", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The challenges brought on by COVID-19 have forced the cancellation of conferences and workshops across the U.S. The High Impact Technology Conference (HI-TEC) face-to-face event scheduled for this year has been cancelled. Prior to canceling the in-person conference, significant efforts had gone into creating a program with 92 accepted presentations, 16 workshops, 20 poster presentations and six special interest group meetings, as well as an exhibit hall. This prior work will be leveraged to support the transformation of HI-TEC to a virtual event. HI-TEC has been produced since 2009 by a consortium of centers and projects funded through the Advanced Technological Education (ATE) program. This conference has provided a national forum for addressing critical issues in advanced technological education. Applications of cutting-edge technology, interactive workshops ,and sessions focusing on a broad range of related topics such as recruitment of diverse and underserved student populations, integration of research into the classroom, faculty development techniques, and best practices in education-industry partnerships have been hallmarks of the conference. This proposal supports transitioning the conference to a virtual event with the theme “Preparing America’s Skilled Technical Workforce.”The virtual conference will support two main goals: 1) Engage and continue to build the technical education and workforce development community; and 2) Deepen sharing and broaden the implementation of evidence-based models and best practices for preparing the skilled technical workforce including an emphasis on post-COVID-19 strategies. The conference executive committee will reach out nationally to the technical education community through digital media. The producing NSF ATE centers and projects will participate in the outreach effort by disseminating conference information to their own networks. Keynote speakers who were identified for the face-to-face conference have agreed to speak at the virtual event. Panelists for an industry panel and an educator panel have been identified. Support will be provided to registrants to convert their presentations to a virtual format, and the event will be held using an online platform. Opportunities for audience interaction will be provided and participant questions will be fielded. Sessions will be recorded and made available after the event. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy.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": "528", "attributes": { "award_id": "2037372", "title": "Response to COVID-19 Field Research and Education Disruptions: Creating Virtual Field Experiences in Coastal and Estuarine Science", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1089, "first_name": "Elizabeth", "last_name": "Rom", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2021-07-31", "award_amount": 97450, "principal_investigator": { "id": 1093, "first_name": "Robert J", "last_name": "Hougham", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1090, "first_name": "Damon P", "last_name": "Gannon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1091, "first_name": "William", "last_name": "Strosnider", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1092, "first_name": "Katherine D", "last_name": "Ryker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Three university field stations, including Upham Woods Outdoor Learning Center at the University of Wisconsin-Madison, the Baruch Marine Field Laboratory (BMFL) at the University of South Carolina, and the University of Georgia Marine Institute (UGAMI), are collaborating to produce a suite of virtual estuarine field experiences polished for distance-delivery in undergraduate programs. UGAMI at Sapelo Island and BMFL at North Inlet - Winyah Bay, will be the two filming locations for these virtual modules because they have unique access to relatively pristine model estuarine systems dominated by Spartina alterniflora marsh that are similar to low-gradient, Spartina marshes that rim the Atlantic and Gulf of Mexico coastlines from approximately New York through Texas, with the exception of the mangrove systems of South Florida. The virtual field experiences will be more accessible and equitable than traditional in-person field experiences and can help address barriers to participation in the traditional field experiences, such as mobility impairments, financial limitations, family obligations, as well as reduce limitations of geographic accessibility. The videos, datasets, and educational materials will be available for free download and will be developed to be compatible with platforms used by universities in virtual learning. Upham Woods’s expertise in environmental education will help ensure the quality and applicability of these assets. Providing these experiences to a wider audience has the potential to broaden the pipeline of students pursuing careers in STEM fields. The final products will feature research by and voices of a diverse group of scientists to address issues of equity in the university field station community and to better represent human diversity in ecological fields, and the modules will be distributed widely to undergraduate institutions, outdoor education centers and schools. This project will develop products that address widespread educational challenges posed by the COVID-19 pandemic for the marine science community. The pandemic has dramatically impacted the educational landscape in the United States, radically limiting the traditional in-person, experiential educational programs for undergraduates and high school students. The crisis has highlighted the need to develop educational content and experiences that provide access to outdoor, field-based learning to all students, not just those who are able to access in-person programs. This project will bring accessible, engaging, and rigorous science experiences to learners by developing a suite of virtual estuarine field experiences for undergraduate courses. These experiences will consist of ten modules, each focusing on a particular estuarine ecology topic and featuring research projects, datasets, prompts, and worksheets to support science learning. A range of topics will be covered in the modules, such as fish ecology, fisheries research field techniques, coastal physical oceanography, coastal depositional processes, biogeochemical processes in the coastal zone, benthic and water column processes, field sampling and laboratory methods for estuarine biogeochemistry research, human interactions with coastal ecosystems, research ethics, formulating research questions and hypotheses, designing field studies, laws governing research, collecting and analyzing data, scientific writing and presentation skills. The modules will also be modified for delivery to high school students as virtual modules that can be incorporated into programs at outdoor education centers and schools.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": "527", "attributes": { "award_id": "2039044", "title": "COVID-19 Postdoctoral Fellowship Program", "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": 1087, "first_name": "Rebecca", "last_name": "Peebles", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-08-31", "award_amount": 343629, "principal_investigator": { "id": 1088, "first_name": "Silvia", "last_name": "Ronco", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 283, "ror": "https://ror.org/00jp8d455", "name": "Research Corporation for Science Advancement", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 283, "ror": "https://ror.org/00jp8d455", "name": "Research Corporation for Science Advancement", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This support to the Research Corporation for Science Advancement (RCSA) helps to stabilize a portion of the scientific workforce by providing approximately four fellowships for highly trained postdoctoral associates in chemistry, materials research, physics, and astronomical sciences. This project is supported by the Divisions of Chemistry, Materials Research, Physics, and Astronomical Sciences in the Mathematical and Physical Science Directorate. COVID-19 has caused unprecedented disruption in many sectors of society. University budgets have been reduced, hiring freezes put in place, and work stoppages enacted. Newer science, technology, engineering, and mathematical (STEM) professionals hoping to enter the professoriate at this time face significant challenges that could force many of them to leave the professions. This activity extends existing RSCA programs to help these highly trained members of the STEM workforce bridge the gap between training and the start of their independent careers, by helping to retain them in STEM while also augmenting the breadth of their experience. Applicants apply through RSCA mechanisms. This activity provides approximately four fellowships for individuals caught in the wake of the COVID-19 crisis that has impacted their prospects on the scientific job market. Rather than have these individuals leave the STEM fields, these fellowships provide them with at least one year of support. In addition to furthering their scientific expertise in areas which they are already familiar, this support provides these postdocs with opportunities to gain pedagogical training in active learning and its implementation in hybrid and online classrooms. This will make these fellows more competitive for academic positions when they return to the academic scientific job market. The mentors of these fellows also benefit since they are able to maintain some stability in their research endeavors. Research funding is stressed due to COVID-19 impacts and during these disruptions, when individuals leave a lab, it is not always possible to hire replacements. These fellowships help mentors retain highly trained and productive researchers at time when training new project members is difficult, if not impossible. Colleges and Universities benefit from these fellowships in that the fellows will become resource people for courses, both now during the fellowships, but also after, when the fellows continue to benefit from the pedagogical insights they gain as they move into their faculty careers.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": "526", "attributes": { "award_id": "2035688", "title": "A Cross-Domain Data-driven Approach to Analyzing and Predicting the Impact of COVID-19 on the U.S. Electricity Sector", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1084, "first_name": "Aranya", "last_name": "Chakrabortty", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 335000, "principal_investigator": { "id": 1086, "first_name": "Le", "last_name": "Xie", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1085, "first_name": "Hongwei", "last_name": "Zhao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims at developing a cross-domain, data-driven approach to tracking and measuring the impact of the ongoing COVID-19 pandemic on the U.S. electricity sector. The COVID-19 crisis has gone beyond anybody’s wildest imagination and is turning out to be a once-in-a-century societal challenge. As the lifeblood of civil society and a key enabling infrastructure system, the electricity sector is quickly adjusting to the new normal, and it is crucial to understand the severity and the resiliency of the grid in response to disruption caused by COVID-19. This project substantiates a data-driven, science-based approach to evaluating the impact of various policy options on the operation of the electric energy infrastructure. Once successfully pursued, the project will provide much needed planning decision support for the electricity sector. The research program will be tightly coupled with an educational effort to train future leaders in the electricity and public health sectors. The research team has engaged female and African American students in building the preliminary version of this data hub and to continue research on the project. The team is also working with the industry members to provide training materials to a broad set of industry affiliatesThe goal of this project is to develop a first-of-its-kind cross-domain data hub and data-driven analysis of the COVID’s impact on the U.S. electricity sector. The approach is to 1) build a comprehensive open-access data hub with quality monitoring and daily updates, 2) quantify the sensitivity of electricity consumption with respect to social distancing and public health policies by using Ensemble Backcast Models and Restricted Vector Autoregression (VAR), and 3) construct a predictive model for the electricity sector considering social distancing policies and mobility in different sectors. The contribution of this project is four-fold. First, this is a first-of-its-kind data hub that combines otherwise unrelated domains of data like electricity markets, public health, and mobility data into a coherent infrastructure. A machine learning-based cleaning and pre-processing technique is proposed. Second, a statistical approach is proposed to quantify the unique impact of a public health crisis on the electricity sector. This entails building and analyzing novel statistical models that encompass societal mobility and public health data into the regression analysis of electricity consumption in major hot spots in the U.S. Third, a novel concept of elasticity of power consumption with respect to societal mobility is proposed and substantiated as an effective indicator of the power consumption as a function of social distancing policy measures. Last but not least, this project will combine all the above three innovations to create a first-of-its-kind predictive model of electricity sector as a function of social distancing policies and public health data. Drawing upon expertise from biostatistics and electric power engineering, this project will contribute to the cross-fertilization between the public health and electric energy sectors.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": "525", "attributes": { "award_id": "2028738", "title": "Rapid: Collaborative Research: Using Data to Understand the Effects of Transportation on the Spread of COVID-19 as a Propagator and a Control Mechanism", "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": 1082, "first_name": "Sandip", "last_name": "Roy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-06-30", "award_amount": 75485, "principal_investigator": { "id": 1083, "first_name": "Philip E", "last_name": "Pare", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The spread of COVID-19 has broad implications both for human health and economies around the world. This Smart and Connected Communities project will monitor the spread of COVID-19 by collecting real-time information on active COVID-19 cases, understand how transportation has driven the spread of the virus, and quantify how travel restrictions have limited the spread of the virus. The data collection will gather and store real-time information on the spread of COVID-19 and a timeline of travel restrictions for three sets of communities. This data will then be employed to model how the virus propagates between communities via transportation using various network-dependent epidemic models. Finally, using the collected data and the calibrated epidemic models, analysis will be conducted to understand how effective the different modifications of the transportation network structure, such as travel restrictions in each set of communities, are at slowing the spread of COVID-19, while factoring in the economic effects. Understanding how the transportation network between communities acts as a propagator of the virus, and how control actions taken by local and national governments to limit or block travel within and between regions slow the spread of the virus will provide the framework for the development of mitigation strategies for the COVID-19 pandemic, as well as other possible outbreaks in the future. These strategies will limit the loss of human life and reduce the economic impacts of the virus. The methods developed as a result of this work will also be beneficial in the future for battling subsequent outbreaks.This project will apply network modeling techniques to understand how different control actions on the transportation network influence the spread of the virus between communities. The understanding gained herein will inform decision makers during this and future outbreaks as to which transportation-related mitigation strategies are best to use in different situations and at what point in the outbreak to use them in order to minimize both the spread of virus as well as the economic impact. The research will draw on and contribute to wide-ranging and fundamental results in statistical data analysis, mathematical modeling and analysis of epidemic processes, mathematical programming, network analysis, and control theory. The resulting study of problems will contribute to advancement of mathematical modeling and analysis of infectious diseases, and mitigation optimization algorithms and heuristics.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": "524", "attributes": { "award_id": "1955260", "title": "CDS&E: D3SC: Developing A Molecular Mechanics Modeling Platform (MMMP) for Studying Molecular Interactions", "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": 1080, "first_name": "Michel", "last_name": "Dupuis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2023-07-31", "award_amount": 500000, "principal_investigator": { "id": 1081, "first_name": "Junmei", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 272, "ror": "https://ror.org/01an3r305", "name": "University of Pittsburgh", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 272, "ror": "https://ror.org/01an3r305", "name": "University of Pittsburgh", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Junmei Wang of The University of Pittsburgh is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop a set of computational tools and build a publicly available platform to facilitate users to study biomolecular systems. The award is cofunded by the Office of Advanced Cyberinfrastructure. High quality molecular mechanics force field (MMFF) parameters are critical to the successful modeling and simulations of various molecular systems. However, users naïve to molecular modeling may find it a daunting task to obtain high-quality MMFF parameters and models without assistance. Dr. Wang and his team are conducting research to develop novel software tools, derive MMFF parameters, build high-quality models, and then integrate them into a freely accessible Molecular Mechanics Modeling Platform (MMMP). MMMP will help users from a broad range of disciplines to study molecular mechanisms of biomolecule-ligand interactions and to calculate the binding affinity accurately and efficiently with ease. Researchers from the drug discovery community can employ MMMP to increase the success rate on the discovery of drug candidates for combating a variety of diseases including the Coronavirus Disease 2019 (COVID-19). A major bottleneck for studying novel molecular systems is the availability, accuracy and validation of consistent molecular mechanics parameter sets. Dr. Junmei Wang is developing a Molecular Mechanics Modeling Platform (MMMP) which integrates force field parameters and residue topologies, novel online tools, and Application Programming Interfaces (APIs) to break the bottleneck. He is conducting research to (1) improve the atom type and bond type perception algorithm to handle arbitrary small molecules; (2) develop molecular mechanics model databases for non-standard amino acid/nucleic acid residues and co-crystallized ligands in the Protein Data Bank, and other compounds (3) develop and advance a software tool coined re-Affinity to bridge the the gap between the efficient docking methods and more computer resource-demanding yet more accurate free energy-based methods; (4) develop a physical, efficient and highly transferrable charge model which can significantly improve the accuracy of free energy calculations; and (5) create a user-friendly Graphic User Interface (GUI), ClickFF, which allows users to generate energy profiles, compare force fields, and optimize force field parameters for selected bonded force field parameters with a few clicks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1405, "count": 14046 } } }