Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1419&sort=-keywords
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However, there is growing concern that STEM graduate education may be falling short on preparing the nation’s workforce to face these global challenges. Equipping students with transformative skills in STEM is necessary for their future success, and for the sustainable development of society. This National Science Foundation Innovations in Graduate Education (IGE) award to Pennsylvania State University will engage underrepresented graduate students in real-world international experiences. Students will develop creative solutions to complex sustainable development challenges as an integral part of team working in close collaboration with local communities. Each year, a cohort of 12 graduate students will advance technical, ecological, and social strategies for sustainable development in 1- to 6-month research internships at different sites around the world. To empower them to effectively and compassionately advocate for change where it is needed most, participants will also receive modular professional development training in science communication, multiculturalism, global leadership, and environmental justice. As a result, students will be better prepared to pursue diverse professional careers in sustainable development, increasing the capacity of the future workforce to solve important environmental challenges. \n\nThe overarching goal of this IGE project is to improve the scientific basis of environmental decision-making, and increase the likelihood of successful implementation of sustainability strategies now and into the future. To meet this goal, this program aims to advance project-based learning through hypothesis-driven contextual systems research in sustainable development. Collaborative, stakeholder-driven research topics intersect at the water-energy-food nexus and include: water treatment and reuse; renewable energy production; nutrient management; sustainable agricultural systems; reducing air pollution and carbon emissions; remote sensing for environmental impact assessment; and climate change mitigation. Sustainable development provides a unifying and motivational theme under which faculty and students can work together across traditional academic boundaries, providing inherent opportunities for teamwork and transdisciplinary collaboration. This initiative is strategically designed to train and empower students that are members of underrepresented groups to develop and mobilize sustainable development solutions together with stakeholders and leading experts. Further, this project will build the foundational principles of transformative graduate education in sustainability that can be replicated at community-engaged institutions everywhere. \n\nThe Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader 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": "10165", "attributes": { "award_id": "2111278", "title": "Collaborative Research: Particles and Proxies for Sampling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 6669, "first_name": "Yuliya", "last_name": "Gorb", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2024-07-31", "award_amount": 149687, "principal_investigator": { "id": 26083, "first_name": "Gideon", "last_name": "Simpson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project addresses sampling in high dimensions which is important for a variety of disciplines, including computational chemistry, materials science, and molecular dynamics simulations for climate models, power network, traffic models, or the study of viruses and pandemics. The project will develop new simulation algorithms as well as improvements of existing algorithms. The outcomes will benefit these disciplines in several ways. First, the algorithmic optimizations will provide new tools that practitioners could use to accelerate their computations. Second, rigorous results on these methods will provide practitioners with confidence in their predictions. Finally, open source software will be developed. Students will be involved and receive interdisciplinary training.\n \nThe project addresses challenges in sampling and related problems arising from complex energy landscapes such as in potential energy in an atomistic system; the negative log-likelihood in a Bayesian inference problem; or the loss function in a machine learning problem. In Markov Chain Monte Carlo methods, these landscapes often define the evolution of a Markov chain that samples some target distribution. This project will develop efficient computations of ergodic averages over Markov chains and methods that reduce the computational cost of ergodic averages, by either reducing the number of required iterations or reducing the per-iterate cost. The new techniques and analyses will be based on proxy landscapes and interacting particle systems. Proxies can reduce per-iterate cost or lead to faster convergence, while interacting particle systems can reduce the bias from proxies or cut down on variance. The project includes a study of how parameter choices affect the variance of the weighted ensemble particle method at finite particle number; the development of a weight-corrected particle system to account for bias from proxies; and an analysis of methods for overcoming sampling difficulties associated with rough landscapes.\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": "10166", "attributes": { "award_id": "2047856", "title": "CAREER: Deep representation learning for exploration and inference in biomedical data", "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": 1357, "first_name": "Wendy", "last_name": "Nilsen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2026-09-30", "award_amount": 586187, "principal_investigator": { "id": 26084, "first_name": "Smita", "last_name": "Krishnaswamy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "Biological systems are inherently complex. Increasingly sophisticated technologies are\nbeing used in biomedical science in order to make sense of this complexity and to understand the\nunderlying factors that cause disease. These technologies generate vast amounts of data in many\ndifferent forms, from changes in how genes and proteins are expressed in individual cells over time,\nto detailed clinical imaging data on large patient populations and whole genome sequencing studies\nacross hundreds of thousands of people. These newly developed datatypes could help uncover\nimportant mechanisms and pathways that underpin health and disease. However, there is a large\ngap between the information contained in these datasets and the ability to extract meaningful\ninsights. Here the PI proposes to address this by developing new machine learning approaches based\non mathematical foundations that will allow us to make sense of these complex datasets. The PI will develop deep representation learning techniques that focus on gaining overall insight into the\nstructures, dynamics, interactions, and predictive features of the data, and will allow \nspecific hypotheses regarding the underlying regulatory mechanisms that drive disease in different\ncontexts to derived. The proposal will also involve training a postdoc, graduate student, and mentorship of local high school students. In addition, it will enable the development of an online workshop to\nwidely disseminate knowledge of unsupervised data analysis to a diverse array of participants from\nacross the country.\n\nThis project proposes to advance biomedical data analysis via three main thrusts. The first thrust is focused on forming deep multiscale representations of the data based on data geometry, graph signal processing, and topological concepts, in combination with powerful, deep learning systems. Such representations will allow for exploration of structure and meaningful, predictive abstractions of the data in a scalable fashion. Our second thrust is focused on integrating multiple modalities of data and organizing multitudes of related datasets using optimal transport and generative models to gain insight into entire cohorts of patients or perturbation conditions. Our third thrust is focused on learning high dimensional stochastic dynamics of the data using neural SDE (stochastic differential equation) and graph ODE (ordinary differential equation) networks to gain insight into underlying gene regulatory networks. We apply our approaches in the context of several specific biomedical challenges. Achieving these aims will enable integration and exploration of a large volume of data for explaining underlying regulatory mechanisms and dynamic phenotypic changes.\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": "10167", "attributes": { "award_id": "2111114", "title": "Collaborative Research: Strategic Course-based Adaptations of an Ecological Belonging Intervention to Broaden Participation in Engineering at Scale", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 604, "first_name": "Eric", "last_name": "Sheppard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2025-09-30", "award_amount": 1731741, "principal_investigator": { "id": 26087, "first_name": "Linda", "last_name": "DeAngelo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26085, "first_name": "Kevin", "last_name": "Binning", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26086, "first_name": "Natascha T", "last_name": "Buswell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 272, "ror": "https://ror.org/01an3r305", "name": "University of Pittsburgh", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by increasing the number and diversity of engineers produced in the United States. Science overall has made great progress in increasing the participation of women. However, engineering has made no overall progress in the last 20 years, with women continuing to earn only 20% of engineering bachelor’s degrees each year. Improving retention in the first two years of engineering programs is important in addressing ongoing attrition. In particular, engineering will become more inclusive when the concerns that many students have about feeling alone in thinking they are incapable of mastering the course’s content are addressed. In this project, short interventions will be implemented. These interventions are intended to reveal to students that most of the students in their class have these same concerns, that previous students just like them with similar concerns have successfully completed this coursework, and that their instructor believes they are capable of succeeding. Prior research by the project team using the intervention in first-year courses has shown that these interventions can entirely eliminate course retention differences by gender as well as by race/ethnicity. A new method for customizing this intervention will be developed, tested, and further improved so that it can have similar strong benefits in different courses and at different universities. Simple interventions that can be easily and scientifically customized to many contexts may have potential for significantly improving engineering outcomes across the United States.\n\nThis project uses an ecological-belonging intervention approach that only requires a one-class or one-recitation session to implement and has been shown to erase long-standing achievement gaps by gender and race/ethnicity in several introductory STEM courses. However, while simple, the intervention cannot involve a fixed script for different university and course contexts. Rather, the content of the intervention needs to be customized to the local context in order to address the specific concerns students have in that specific context. This project brings a highly interdisciplinary team across three strategically-selected universities with the goal of developing an approach to identify which 1st and 2nd year courses need this intervention, reveal student concerns in that course, adapt the intervention to address those concerns, and address other pragmatic constraints of how that course is taught. This systematic approach also includes processes for onboarding all the instructors of the given course. In answering a set of seven core research questions, the project intends to expand knowledge about 1) where (on which outcome variables), when (in which contexts, for which students), and why the ecological belonging intervention has positive effects, and 2) the extent to which this intervention on its own has measurable impacts on the overall problem of representation in the larger challenge of representation within the large engineering pathways that have struggled with representation. This kind of foundational knowledge is critical to making decisions about when to apply the intervention as well as providing important insights into how to apply the intervention. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.\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": "10168", "attributes": { "award_id": "2114791", "title": "FW-HTF-P/Collaborative Research: Exploring Tools to Help Workers and Organizations Adapt to AI-enabled Robots", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "FW-HTF Futr Wrk Hum-Tech Frntr" ], "program_reference_codes": [], "program_officials": [ { "id": 2859, "first_name": "Balakrishnan", "last_name": "Prabhakaran", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2021-12-31", "award_amount": 77198, "principal_investigator": { "id": 8917, "first_name": "Erik", "last_name": "Brynjolfsson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will promote exploration of scalable tools to aid workers and organizations adapt to artificially-intelligent robots. In a sharp departure from current robotic systems that have to be programmed for a single manipulation task in a very tightly constrained set of conditions, venture-funded firms are designing and beginning to test qualitatively new robotic technologies that promise to flexibly automate entire classes of embodied tasks in widely divergent conditions. In this likely future, robots will adapt as readily to new repetitive manual tasks as a modern microprocessor adapts to new computational tasks. Such \"learning\" robots would clearly have profound implications for workers and organizations, but previous research on automation offers only limited guidance on how they will adapt. The researchers have recently begun a nationwide, four-year field study that will identify edge cases in which organizations and low-skill workers achieve unlikely yet systematic success, given the introduction of this disruptive technology. This will allow deriving design constraints for potential solutions from grounded theory, centering on the hard-won, demonstrably successful innovations of a suitably-diverse pool of informants. While existing research stands to unveil the mechanisms behind rare, in vivo learning successes to the world, this FW-HTF-P (Future of Work at the Human-Technology Frontier - Planning) award will assemble a world-class team of researchers who are committed to trying to expand and capitalize upon these mechanisms via new tools. This research has high-impact potential for organizations, lower-skilled workers and policy makers on how to expand and enrich work involving increasingly intelligent systems in the 21st century.\n\nWith AI in robotics as the technology, humans collaborating with robots as the workers, and organizations employing both the robots and the workers as the context of work, the team of researchers will specifically contact and convene a group of top experts in diverse technical domains including social media, massive open online courseware, crowdsourced knowledge repositories, peer assessment and coaching, user experience design and platforms for on-demand labor, crowdsourcing and innovation challenge execution. Beyond these technical disciplines, the researchers will invite policymakers and technologists, as the pathways to local success will likely be deeply intertwined with legal and commercialization processes. The researchers will begin by sharing very preliminary findings, research questions and objectives from the current study with a select group of such researchers who may have interest in a potential collaboration. The researchers will then extend formal invitations to a workshop to no more than ten potential collaborators. This workshop will be one day in length and will be described as an opportunity to explore and decide upon potential collaborative opportunities related to helping workers and organizations adapt more productively to general-purpose robots. The researchers will explore potentially new organizational theories that take perspectives such as: (a) accounting for success as a learning problem in which robots, workers and organizations learn from each other; (b) the character of learning infrastructures evident in various practices for adapting to learning machines acting as co-workers; (c) how the organization of such learning practices impacts skill changes, role transformations, as well as workers and organizations. The researchers will then solicit participants' input and commitment for tools to scale the successes inherent in the findings and select the tool likely to have the greatest benefit for the most Americans. The researchers will then jointly craft an FW-HTF-R (Future of Work at the Human-Technology Frontier - Research) proposal with interested collaborators that reflects a rigorous test of this tool in real-world settings. The ultimate goal of this project is to develop the necessary research personnel, research infrastructure, and foundational work to expand the opportunities for studying future technology, future workers, and future work at the level of a FW-HTF full research proposal.\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": "10169", "attributes": { "award_id": "2131408", "title": "Understanding Thermal Energy Scavenging in All-Inorganic Perovskite Nanocrystals", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "ELECTRONIC/PHOTONIC MATERIALS" ], "program_reference_codes": [], "program_officials": [ { "id": 6094, "first_name": "Paul", "last_name": "Lane", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-11-01", "end_date": "2024-10-31", "award_amount": 465000, "principal_investigator": { "id": 26088, "first_name": "Matthew", "last_name": "Sheldon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 342, "ror": "https://ror.org/01f5ytq51", "name": "Texas A&M University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "NON-TECHNICAL SUMMARY:\n\nThe industrial revolution was enabled by heat engines that perform work by converting thermal energy (heat or high temperature) to mechanical energy. Recently, several advantages have been theorized for heat engines that do work by converting heat into light, similar to how traditional engines use fluids or gases such as steam. However, there are very few known optical materials that can efficiently convert heat to optical energy, in part because heat degrades their optical performance. This project will prepare new classes of materials with precise structural and chemical properties on the nanoscale to allow for efficient conversion between thermal and light energy. The research team will examine how increasing the thermal energy in these materials can also result, unusually, in an increase of the energy of light that they emit. This phenomenon can ultimately lead to significantly more efficient heat engines with no moving parts, better solar cells, or new methods of refrigeration that do not require compressed gasses or mechanical components. The project will support graduate and undergraduate research students working in the PI’s laboratory as well as the development of novel curricula and technological tools for teaching large-format freshman chemistry courses. The primary investigator will refine some of the best innovations developed during the pandemic and take advantage of these for the transition back to classroom instruction.\n\nTECHNICAL SUMMARY:\n\nThis project will study thermal energy scavenging by one-photon optical upconversion, also known as anti-Stokes photoluminescence. Upconversion results when heated photoluminescent materials emit band-edge photons during subgap excitation, while simultaneously decreasing in temperature. Inorganic lead halide perovskite nanocrystals are a champion materials system for efficient one-photon upconversion, but fundamental details of the mechanism are unknown, impeding rational strategies for further development. Spectroscopic studies conducted by the PI’s team will elucidate a clear mechanism for optical up-conversion, as well as outline the structure-property relationships that define the absorption cross section, bandwidth, temperature response, and the fundamental limits on efficiency. The research team will vary composition and morphology during nanocrystal synthesis. Structural parameters such as crystal phase, shape, and surface-to-volume ratio will be tracked using high resolution transmission electron microscopy, and powder X-ray diffractometry. In parallel, the team will perform photoluminescence excitation spectroscopy and photoluminescence lifetime studies. These experiments will quantify the dependence on above-gap or below-gap excitation wavelength, power density, and nanocrystal temperature to identify the unique states that mediate the interconversion of vibrational and electronic excitations, while preserving the intrinsic, near-ideal luminescence efficiency of the nanocrystals. The overarching goal is to understand the thermal energy scavenging properties of inorganic lead halide perovskite nanocrystals to create luminescent materials that can aid thermal-to-optical energy conversion, optical up-conversion, and optically driven refrigeration.\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": "10170", "attributes": { "award_id": "2055077", "title": "Strengthening the Industry 4.0 Workforce through Virtual Reality Training Modules", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [ { "id": 2366, "first_name": "Mary", "last_name": "Crowe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2021-10-31", "award_amount": 593464, "principal_investigator": { "id": 26091, "first_name": "Jason", "last_name": "Simon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26089, "first_name": "Sheri", "last_name": "Plain", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26090, "first_name": "Leslie T", "last_name": "Ashton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1892, "ror": "", "name": "Owensboro Community & Technical College", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "As manufacturing contributes to higher export potential, better standards of living, and more jobs in America, addressing the need for a skilled technical workforce is crucial to support the future economic prosperity of the country. Moreover, as a result of the multiplier effect, manufacturing also impacts the broad economy, with every 100 jobs in a manufacturing facility creating 250 jobs in other sectors. In 2018, Deloitte and the Manufacturing Institute conducted a study on the manufacturing skills gap that revealed artificial intelligence, advanced robotics, automation, analytics, and the Internet of Things are emerging to transform the world of work (Industry 4.0) and are likely to create even more jobs than they replace. Since mid-2017, job openings in manufacturing have grown at double-digit rates, with a growing gap between open jobs and an available skilled talent pool to fill them. To help bridge this gap, this project of the Advanced Manufacturing Technical Education Collaborative (https://amtecworkforce.org/) will create a virtual reality (VR) application built on the zSpace platform that students can use to troubleshoot ten scenarios on an industry simulator. These scenarios will be integrated into a credit-bearing post-secondary capstone course for manufacturing students developed and designed with input from industry and education subject matter experts. \n\nThis partnership between representatives of industry and advanced technological education will ensure that the project strengthens the competency and global competitiveness of the advanced manufacturing workforce. The VR application and curricula will be field tested at targeted Advanced Manufacturing Technical Education Collaborative partner sites across the country, as well as with students and instructors currently using zSpace’s virtual reality platform. In addition, an exploratory curriculum will be developed to engage K-12 students in gaming-like simulations to recruit youth into advanced manufacturing technical training. Several objectives will guide the execution of the project. First is to engage secondary students in Industry 4.0 advanced manufacturing concepts through field-testing a newly created virtual reality “game-like” application. Second is to train post-secondary students preparing to be manufacturing technicians to enter the workforce with a basic understanding of Industry 4.0 technologies and the ability to apply them successfully in the workplace setting. Third is to increase the aptitude of secondary and post-secondary faculty in Industry 4.0 concepts and the use of virtual reality technology by providing comprehensive professional development. Three primary contributions to the field are anticipated. One is an expanded set of partnerships between academia, industry, and others to develop technician training that aligns with the growing Industry 4.0 infrastructure. Two is the improvement of secondary and post-secondary student learning in emerging Industry 4.0 technologies and virtual reality applications. Third is the lessening of disruptions of manufacturing technician training during a pandemic or similar event that creates a need for remote learning. 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. As a result, the project has the potential to contribute to improving the national STEM workforce.\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": "10171", "attributes": { "award_id": "2120098", "title": "Collaborative Research: SaTC: CORE: Large: Rapid-Response Frameworks for Mitigating Online Disinformation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Secure &Trustworthy Cyberspace" ], "program_reference_codes": [], "program_officials": [ { "id": 1740, "first_name": "Sara", "last_name": "Kiesler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2026-09-30", "award_amount": 748437, "principal_investigator": { "id": 26092, "first_name": "Jeffrey", "last_name": "Hancock", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Disinformation is a critical, pressing challenge for society. It diminishes our ability to respond to crisis events, including acts of terrorism and pandemics. It makes us vulnerable, as individuals, groups, and a society, to manipulation from foreign governments, financial opportunists, and a range of other bad actors. This problem, exacerbated by the design and widespread use of social media platforms, is inherently a problem of trust — disinformation undermines trust in information, science, democratic institutions, journalism, and in each other. This research advances our understanding of online disinformation and applies innovative approaches and collaboration infrastructure to address this challenge at a sophistication and pace on par with the dynamic and interdisciplinary nature of the challenge. Through the development, implementation of rapid response frameworks, the research team rapidly identifies disinformation campaigns and communicates those findings uniquely to diverse stakeholders in government, industry, media, and the broader public — helping to build societal resilience to this kind of manipulation.\n\n\nThis research has three integrated components: 1) developing models and theories of how disinformation is seeded, cultivated, and spread that take into account the sociotechnical nature of the problem; 2) developing and applying innovative, rapid-analysis frameworks for responding to disinformation quickly; and 3) implementing and evaluating the impact of multi-stakeholder collaborations to address disinformation in real-time during real-world events. The work applies a mixed-method approach that integrates novel visualizations and network analysis to identify patterns and anomalies with qualitative analysis that reveals the meanings of those features. Extending from a rapid response approach, investigators are also developing and evaluating, using interviews and experiments, strategies for communicating these findings with diverse stakeholders. Conceptually, this research leverages theories of rumoring from sociology and social psychology and the growing body of literature related to online manipulation to shed light on the participatory dynamics of disinformation campaigns. In terms of impacts on scientific infrastructure, this effort builds collaboration frameworks that others can use to create their own systems for rapid response.\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": "10172", "attributes": { "award_id": "2117976", "title": "RUI: Household and institutional responses to security threats from environmental and economic disturbances", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Human-Envi & Geographical Scis" ], "program_reference_codes": [], "program_officials": [ { "id": 1931, "first_name": "Jeremy", "last_name": "Koster", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-15", "end_date": "2024-12-31", "award_amount": 285722, "principal_investigator": { "id": 26094, "first_name": "Christopher", "last_name": "Bacon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26093, "first_name": "William A", "last_name": "Sundstrom", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 690, "ror": "https://ror.org/03ypqe447", "name": "Santa Clara University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). \n\nThis project studies how small-scale farmers, communities, and institutions can exhibit more resilience in the face of a complex and changing set of environmental and economic challenges. These challenges include droughts, hurricanes, and natural disasters, crop diseases, political instability, fluctuations in food and agricultural prices, pandemics, and problems arising from low socioeconomic status. A strategy for mitigating the detrimental effects of these extreme events is to practice diverse livelihoods while decreasing dependence on a single crop or commodity. This project examines the effects of diversification on farmers’ economic outcomes while also assessing whether diversification enhances biodiversity in ways that improve crop harvests, food security, and pest control. The research provides insights about economic livelihoods that are relevant to populations of farmers in diverse regions, including areas that contribute much of the cultivated food that is consumed in global markets. The project also advances the training and education of university students who will participate in the research.\n\nMuch remains to be learned about the effectiveness of diversification and about how farmers, communities, and organizations can experiment with new approaches and share promising practices. To address these outstanding questions, the investigators develop a participatory approach to identify and assess the effects of diversification practices on multiple outcomes, including disaster risk, livelihood capabilities, food security, dietary diversity, gender equity, water security, and food sovereignty. The investigators also examine the processes of innovation, learning, and farmers’ motivation for adopting new diversification activities. Methodologically, the project integrates field research with concepts and analytical techniques from multiple academic disciplines, including agroecology, political ecology, development studies, and economics. This study also identifies how farmers with different diversification practices responded to recent natural disasters, showing the consequences of variation in diversification. Insights from this research can be used by institutions who are responsible for guiding and recovery efforts following natural disasters. The findings will also be beneficial for multinational efforts to promote sustainable agriculture in diverse settings.\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": "10173", "attributes": { "award_id": "2131959", "title": "3rd Enterprise and Infrastructure Resilience Workshop", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Proc Sys, Reac Eng & Mol Therm" ], "program_reference_codes": [], "program_officials": [ { "id": 9480, "first_name": "Catherine", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2022-07-31", "award_amount": 13195, "principal_investigator": { "id": 26096, "first_name": "Debalina", "last_name": "Sengupta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26095, "first_name": "Izabela", "last_name": "Balicka", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 816, "ror": "https://ror.org/02yjcxz86", "name": "American Institute of Chemical Engineers", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The engineering systems designed today are increasingly complex and dependent on other surrounding systems. Due to this complexity and interdependence, it is often impossible to predict all disruptive events that can be expected in the system’s lifecycle, which may even lead to economically impactful disruptions and critical failures. Resilience is a concept encompassing various disciplines, and the creation of a resilient system will enable the system to recover from unpredicted disturbances and adapt to these new conditions. Efforts to improve resilience of chemical supply chains, chemical plants and energy networks will benefit society in by providing techniques and approaches that are needed to quickly respond to rapidly changing conditions. Knowledge from topics covered in this workshop is expected to improve our economy's and society's ability to recover from or adapt to large scale disasters, pandemics, stronger hurricanes, and deep freeze from winter storms. Further, these discussions will emphasize sustainability and safety throughout development. This virtual workshop will be advertised to a diverse audience. The NSF funds will be used to support student, post-doctoral fellow, and early-career researcher attendance, for those who would not otherwise be able to afford the registration. \n\nIncorporating concepts from resilience engineering in the design of systems such as chemical plants, chemical supply chains, and energy networks would be very effective in minimizing undesired effects of unforeseen disturbances yet requires expertise from various disciplines in science and engineering such as chemical engineers, sustainability engineers, mathematicians, and more. This workshop explores multifaceted resilience strategies for the modern enterprise that address dependence on external systems, such as the environment, stakeholders, shareholders, and society. The workshop would bring together professionals from various fields that are working in academia, industry, and government to have a wide range of perspectives. Such a combination of attendees has the potential of fostering impactful discussions and potential collaborations that would serve to further advance the field. Topics to be integrated at this workshop include risk management, optimization, operations research, sustainable engineering, critical infrastructure, process supply chains, computational models, machine learning methods, multi-scale systems analysis tools, and process safety.\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": 1419, "pages": 1424, "count": 14236 } } }