Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-award_amount
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NNA projects address convergence scientific challenges in the rapidly changing Arctic. The Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by supporting planning activities with clear potential to develop novel, leading edge research ideas and approaches to address NNA goals. It integrates aspects of the natural environment and social systems, and addresses important societal challenges, builds significant educational opportunities, and engages internationally and with local and Indigenous communities. Rapidly changing Arctic conditions necessitate multi-perspective approaches to creating new knowledge and engaging communities that are most affected by these changes. Thus, planning and co-producing effective Arctic research is needed, results of which will inform social and ecological security on a national, and global scale. The Kluane Lake Region in the Yukon Territory has recently experienced many abrupt environmental shifts. In 2016, the retreating Kaskawulsh Glacier cut off flow from Lhù’ààn Mân (Kluane Lake), effectively removing one of the largest water inputs to the lake. Additionally, recent insect outbreaks have harmed nearby forests, and warming climate regimes have shifted winter ice formation and snowpack development. While these compounding environmental changes will have drastic impacts to the ecosystem for decades to come, their impacts on the local communities will be more rapid. While these ecological impacts have been observed by formal research communities, it is also critical to work closely with local communities to understand their perspectives on critical research questions and natural resource concerns. Thus, the research team works with local communities like the Kluane First Nations and Champagne and Aishihik First Nation, as co-producers of research questions and design. This convergence research team is co-creating research questions, seeking questions that are relevant and generalizable to the Kluane Lake region specifically, the Yukon generally, and broadly at a pan-Arctic scale. Research questions are being identified by incorporating the experiences and local knowledge of communities. Specifically, planning activities center around a community liaison employed by the project with credibility in the communities around the lake, with two kick-off scoping trips (research team), and then a community-driven workshop to identify gaps in current understanding and existing methodologies to develop: 1) a citizen-science monitoring program to generate reliable and consistent data for climate and lake conditions; 2) compilations of datasets, databases, and tools for accessibility and integration into educational offerings; and 3) knowledge of community acceptance and perspectives on new technologies and data tools. Researchers are employing a mixed methods triangulation approach during the co-production process, via assembly of existing quantitative data, key informant interviews, and structured and unstructured qualitative data collection during the workshop. Researchers will then expand the team to include gaps in expertise, based on final co-produced research questions. Finally, the research team is involving underrepresented students from multiple institutions in the planning process, so they may see how a hybrid model of knowledge co-production (traditional and westernized view of knowledge) takes place.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": "12641", "attributes": { "award_id": "2142327", "title": "Distributed Learning for Undergraduate Programs in Data Science at Diverse Universities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28561, "first_name": "Jonathan", "last_name": "Spector", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 253, "ror": "https://ror.org/00v97ad02", "name": "University of North Texas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by improving undergraduate education in data science. This project will develop and deliver ten Data Sciences (DS) courses to students from a consortium of eleven diverse universities by using a flexible distributed learning (DL) platform. This consortium will provide increased opportunities for DS instruction at institutions with limited infrastructure and resources, including seven minority-serving institutions. The courses will adapt the United States military's advanced DL technology to an academic setting in order to harness the power of artificial intelligence (AI) in tailoring optimal learning experiences for the specific needs of each individual student. Pervasive DL technologies help to overcome inefficiencies found at individual institutions due to small enrollments and limited faculty expertise. At least two hundred undergraduates will gain research experiences from taking the consortium's DS coursework, participating in a summer research workshop, and obtaining a DS consortium certification. To broaden this project’s overall impact on equal learning opportunities and social mobility this project will recruit students from diverse backgrounds.The project aims to implement data-driven pedagogical research on innovative DL practices across diverse universities through the use of adaptive distributed learning (ADL). The difference between DL and ADL courses is that the latter utilizes the interoperable data exchange standard of the U.S. Department of Defense to leverage the power of AI, big data, and communication technologies. ADL provides learning that can be personalized and delivered anytime and anywhere to an individual student. The adaptation of ADL technologies in an academic setting remains largely untested and would benefit greatly from an analysis of its efficacy. The consortium is organized into four organizational clusters headed by Embry-Riddle Aeronautical University (FL), the University of North Texas, and Florida A&M University. Institutions within each cluster include Bethune-Cookman University (FL), California State University at Los Angeles, Hampden-Sydney College (VA), Jackson State University (MS), Jarvis Christian College (TX), Lane College (TN), Morgan State University (MD), and Simmons University (MA). Leveraging the combined physical and intellectual resources of this alliance of diverse institutions with DL technology provides students at these institutions with the opportunity to pursue DS training on par with what would be expected in a research university setting, thereby removing barriers that may exist for these students to prepare for competition in the STEM job marketplace. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12642", "attributes": { "award_id": "2247357", "title": "CAREER: Socially-Aware Language Technologies To Support People in Supporting Others for Better Online Communities", "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": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28562, "first_name": "Diyi", "last_name": "Yang", "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": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This research will invent a set of new socially aware language technologies designed for online peer support groups, including machine learning classifiers that automatically predict helping skills from text in noisy and low-resourced settings, and language generation techniques that effectively generate tailored and contextualized assistance for supporters. The people who provide support are the key to the success of these online groups, which are used by millions of people with health concerns. However, online supporters often do not receive rigorous training and tailored feedback, which might lead to unsupportive or even negative helping behaviors. Existing mechanisms of training or scaffolding largely rely on human supervision, making it hard to scale up to help the large number of supporters who support millions of people in need of care. This work has the potential to advance the state-of-the-art and scale up to many different other domains with minimal human effort. By developing, deploying, and evaluating new interventions that empower supporters in socially important domains, this work broadens the scientific understanding of technology use for mental health peer support. By combining computer science with the study of online peer support groups, this work will appeal to students who might not otherwise be attracted to science and engineering careers, including women and members of underrepresented groups.This work will accomplish the vision of supporting people in better supporting others in several representative text-based online peer support groups by: (1) developing innovative natural language processing techniques to predict supporters' helping skills and examining how helping skills related to positive outcomes; (2) designing contextualized language generation approaches that provide tailored assistance for supporters by highlighting which helping skills are needed in a given situation and suggesting example responses with actionable feedback; and (3) creating an open-source and human-in-the-loop tool to empower supporters and evaluating how the tool can be used for both training and real-time scaffolding via lab studies, field experiments, and real-world deployment. The result will be a novel synthesis of social science theories and beneficial advances in natural language processing (NLP) communities, to pioneer this emerging research field that uses NLP to support mental health and well-being. Concretely, it will develop scientific knowledge of how supporters use different helping skills to help seekers, and a deep understanding of how such support exchange relates to positive outcomes.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": "12643", "attributes": { "award_id": "2221335", "title": "Collaborative Research: Pacific Alliance for Low-Income Inclusion in Statistics & Data Science", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28563, "first_name": "Soumik", "last_name": "Pal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of California - Santa Barbara, the University of California - Irvine, the University of Washington, California State University, East Bay, California State University, Monterey Bay, San Diego State University, and California Polytechnic State University, San Luis Obispo. The Data Revolution is generating numerous well-paid career paths, and creating a significant workforce shortage, in Statistics & Data Science. Graduate degrees are needed for many lucrative, data-rich careers, which can represent a significant barrier for low-income students. This project will provide scholarship support to approximately 115 talented, low-income undergraduate students studying statistics and data science and provide continued scholarship support for at least 65 of them over two years of graduate studies. Scholars will benefit from faculty and peer mentoring, an annual meeting that spans all seven participating schools, and support for applying to and preparing for graduate school, including a pre-grad summer bootcamp. Additional academic supports include a small-group directed reading program, shared coursework to build community within scholar cohorts, and undergraduate research opportunities.The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Additional project goals and aims include: (a) to offer a cohort-based program that supports students financially via scholarships lasting up to 3 years; (b) provide scholars with academic and co-curricular experiences designed to facilitate placement into careers in statistics and data science; and (c) offer interventions to enhance scholars’ community cultural capital. Project research will use surveys and interviews to study three main themes: (a) how counterspaces and other kinds of community develop and support scholars’ progress towards their goals; (b) how scholars’ community cultural wealth shapes and is shaped by the counterspaces and communities that develop; and (c) how students’ low-income status and other identities impact key counterspaces and communities and influence scholars’ choices and outcomes. Project evaluation will provide formative and summative feedback on all aspects of the project to support efficient progress towards goals. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12644", "attributes": { "award_id": "2221136", "title": "Leveraging Data Science to Promote the Successful Participation of Talented, Low–Income, Undergraduate Students from Rural Alabama in STEM Fields", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28564, "first_name": "Janice", "last_name": "Case", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2125, "ror": "https://ror.org/014wfj781", "name": "Jacksonville State University", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will contribute to the national need for well–educated scientists, technicians, engineers, and mathematicians by supporting the retention and graduation of high–achieving, low–income undergraduate students with demonstrated unmet financial need at Jacksonville State University, a comprehensive, regional four–year university in rural, northeast Alabama. Over a six–year duration, the project will provide scholarships to 20 selected undergraduate students each year who are pursuing bachelor’s degrees in Biology, Chemistry, Computer Science, Computer Information Sciences or Mathematics. Selected student applicants who are First Year, Sophomores, Juniors or Seniors will receive an annual scholarship to cover two semesters. At least 10, $10,000 scholarships will be awarded each year together with 10 or more $5,000 scholarships. An estimated 120 scholarships will be awarded overall. The project aims to increase student persistence in science, technology, engineering and mathematics (STEM) fields by linking scholarships with proven and effective supporting activities, including faculty advising, faculty mentoring, faculty support, faculty–guided undergraduate research experiences, graduate school preparation, travel to research universities and high–tech organizations and STEM private businesses and appropriate science–focused summer internships. Project staff, together with faculty mentors, will assist scholarship recipients in developing Individual Development Plans outlining the scholarship recipient’s career goals and progression to achieving such goals. The project will also engage in improving student retention in STEM majors and programs. By establishing a system to support scholarship recipients, the project will test new approaches to prepare and provide STEM career-ready professionals for the STEM industry. Project findings will also generate new knowledge for actionable integration to impact rural institutions targeting high–achieving, low–income undergraduate students for enrollment into NSF approved STEM majors.The project's primary outcome is that within six years, each scholarship recipient will graduate with a JSU data science minor or data science concentration and a STEM bachelor’s degree and enter the STEM workforce within an occupation of regional, statewide, or national need for STEM professionals or pursue a graduate program in STEM. The specific aim of the project is to positively address several gaps reported by STEM industry which are affecting the STEM workforce. (1) A fundamental skills gap is the ability to adapt, critically think, and conduct complex creative problem-solving. (2) The belief gap is the incorrect beliefs regarding what traits are needed to be successful in STEM. (3) The postsecondary education gap occurs because colleges are producing fewer STEM graduates than are needed in the workforce. This project is designed to address each of these gaps through the provision of scholarships and intervention services to support the STEM degree pursuits of diverse students. In addition, the project will also investigate the role of psycho-social factors and the efficacy of targeted intervention for addressing the identified critical gaps in skills and beliefs among scholarship recipients. The project will explore if positively addressing critical gaps in access, participation, skill development, and beliefs will result in scholarship recipients' successful completion of a program of study in data science and STEM, and in being fully prepared for postgraduate STEM study and/or entry into the STEM workforce. The project will advance knowledge and understanding about the effectiveness of recruiting and supporting undergraduate students with academic ability, talent, or potential, who are low–income and with a demonstrated unmet financial need to complete a data science minor or data science concentration and pursue an NSF approved STEM discipline. The project will be evaluated internally, by a project staff member with a lens on identifying generalizable knowledge, and formatively and summatively by an external evaluation team. Results of the project will be shared by the project team and external evaluation team at regional conferences, in journal articles, in local and regional newspapers, through social media, and via a project website. The project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics Program, which seeks to increase the number of low–income, academically talented students with demonstrated unmet financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low–income students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12645", "attributes": { "award_id": "2153397", "title": "CRII: SaTC: Towards Secure and Privacy-preserving Input on Augmented Reality Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Secure &Trustworthy Cyberspace" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28565, "first_name": "Jiacheng", "last_name": "Shang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 333, "ror": "https://ror.org/01nxc2t48", "name": "Montclair State University", "address": "", "city": "", "state": "NJ", "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).Augmented reality is a type of interaction and immersive experience that overlays the real-world scene with virtual objects. To deliver such an experience, augmented reality devices rely on various sensors to sense the user and surrounding environment accurately and synchronously. However, the sensors suffer from various attacks, which further negatively affect the augmented reality experience. The goal of this project is to have a complete understanding of feasible attacks on all sensors in augmented reality devices and propose new defense algorithms against found attacks. The outcome of the research will improve the security and privacy of augmented reality devices.To accomplish this goal, the project focuses on (i) identifying feasible sensor injection attacks and proposing defense algorithms with behavior knowledge and sensor fusion, (ii) studying what sensitive information can be inferred using the existing sensor data on augmented reality devices, and (iii) proposing an inference prevention model in the operating system to filter out sensitive information in sensor data before sending them to augmented reality applications. The outcome of this work can provide insights for building secure and privacy-preserving augmented reality applications and devices. This project will also engage graduate, undergraduate, and K-12 students to develop their interests in computer security and privacy.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": "12646", "attributes": { "award_id": "2228923", "title": "EAGER: Exploring Applications of Graph Theory for Improved Understanding and Predictability of Atmospheric Chemistry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Atmospheric Chemistry" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28566, "first_name": "Sam", "last_name": "Silva", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 152, "ror": "https://ror.org/03taz7m60", "name": "University of Southern California", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This EAGER project focuses on the development of tools for simplifying the representation of complex atmospheric chemical reactions in air quality and climate models. Several mathematical techniques will be used to develop and assess reduced-complexity representations of atmospheric chemical mechanisms to enhance the research community’s ability to study atmospheric chemistry and the role it plays in causing air pollution and climate change. This effort is expected to provide new tools and approaches to characterizing, utilizing, and understanding complex chemical systems in the atmosphere.The two science objectives for this proposed work are: (1) to quantify the structural and dynamical properties of atmospheric chemical reaction mechanisms using novel graph theoretical techniques to enable new scientific insights; and (2) to apply these graph theoretical techniques to support the development and assessment of reduced-form models of atmospheric chemistry, leveraging both traditional graph clustering methods and modern graph machine learning. The first objective will use techniques including motif analysis, path and cycle analysis, and network robustness metrics, while the second objective will use Louvain clustering and graph machine learning techniques.This proposal meets the EAGER criteria because the application of methods derived from graph theory to atmospheric chemical mechanisms development and evaluation is largely untested and has the potential to transform our ability to understand complex and reduced-form chemical mechanisms. The current process of developing and evaluating atmospheric chemical mechanisms is complex, time consuming, and contains substantial subjective decision making. By taking established methods from graph theory, this work could provide new tools and new approaches to characterizing, utilizing, and understanding complex chemical systems. This project is a high-risk, high-reward project as the methods proposed have only been applied to chemical mechanisms in preliminary work and so the success of the proposed work is difficult to predict. This effort has the potential to provide transformative tools that could further our understanding of existing and to-be-developed atmospheric chemical mechanisms.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": "12647", "attributes": { "award_id": "2317471", "title": "CAREER: Photonic Quantum Machine Learning: From Architecture to Applications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28567, "first_name": "Zheshen", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "The formulation of quantum mechanics in the 20th century shifted the landscape of science, giving birth to a plethora of revolutionary technologies that empower the information age. Humankind is now on the verge of a second quantum revolution fueled by quantum information science (QIS), which is envisioned to enable disruptive communication, sensing, and computing applications. Despite the tremendous prospects promised by QIS, building large-scale and robust quantum information processing systems remains an outstanding challenge due to the fragility of quantum information in the present noisy intermediate-scale quantum (NISQ) hardware. To unlock the power of NISQ devices and systems, hybrid quantum-classical protocols have become a focus of recent QIS studies, in which state-of-the-art classical data science tools are leveraged to steer NISQ hardware towards solving specific data-processing problems. This CAREER project will develop a new photonic quantum machine-learning architecture that combines mature, classical machine-learning tools and NISQ platforms to endow unprecedented communication, sensing, and data processing capabilities. Compared with other NISQ platforms, quantum photonics feature room-temperature operations, mass productivity, and compatibility with the existing telecommunication and sensing infrastructures. The project will advance basic knowledge for the NISQ era and the interdisciplinary areas of QIS, machine learning, and NSF’s 10 Big Ideas Harnessing the Data Revolution and the Quantum Leap. A critical ingredient for a sustainable QIS ecosystem is to develop the next-generation quantum workforce. To this end, this CAREER project will encompass activities for: 1) QIS teaching laboratories for undergraduate students; 2) a QIS training program for industry workforce development; and 3) outreach to engage K-12 STEM students early in QIS.The research activities of this CAREER project will encompass both: 1) a photonic quantum machine-learning architecture based on a classical machine-learning framework and photonic quantum information-processing hardware, including reconfigurable entanglement sources and adaptive quantum receivers; and 2) photonic quantum machine-learning applications for long-haul optical communications, multi-domain sensing, and quantum-enhanced data processing. The new photonic quantum machine-learning architecture will effectively use cutting-edge classical machine-learning tools to configure variational photonic quantum circuits, as a powerful means to generate, process, and measure quantum information. Although photons interact only weakly with each other to hinder the use of large-scale photonic entanglement, the proposed photonic quantum machine-learning architecture will overcome this barrier by leveraging quantum photonics that offer deterministic generation, the processing of large-scale entanglement, and suitability for sensing and communication applications. By combining enhancements from machine learning and quantum coherence, the expected project outcomes will enhance various sensing- and communication-related tasks, including pattern recognition, deep-space signal detection, and efficient data compression. Ultimately, broadly sharing the new knowledge grown out of these research activities should spark collaborations between academia, National Laboratories, and the U.S. healthcare, aerospace, environmental protection, and chemical engineering industries. By working with industrial partners, the CAREER project will connect with new quantum technologies that transform U.S. industries. The project research facilities and workforce development activities will help to prepare the U.S. technology industry workforce for the future quantum edge and establish comfortable and personally relevant quantum foundations for students across university to high-school settings.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": "12648", "attributes": { "award_id": "2214224", "title": "Doctoral Dissertation Research: Disentangling causes and consequences of religious conversion", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Cult Anthro DDRI" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 5574, "first_name": "Cristina", "last_name": "Moya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 276, "ror": "", "name": "University of California-Davis", "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). Why do people adopt a novel set of religious beliefs and practices, especially if it is a minority one and doing so is potentially costly? This doctoral dissertation research project explores the conditions that make it more likely for someone to convert to a new religion and measures the consequences of conversions for cooperation across religious and ethnic boundaries. Results can help us understand the underlying drivers of recent large-scale shifts in religious affiliation, and their potential consequences for inter-group cooperation and market integration. Additionally, this project will provide research opportunities for graduate and undergraduate students, offer methods training to underprivileged academic communities, and disseminate results to both English and Spanish-speaking audiences. By using mixed methods in an ethnographic context experiencing high rates of conversion from Catholicism to various forms of Protestantism, this study can help adjudicate between the relative contributions of material motivations, assortment along shared values, and social learning heuristics in driving conversion decisions. This will include conducting retrospective semi-structured interviews, vignette experiments, economic games, and analysis of longitudinal census data. Economic approaches to conversion highlight the economic outcomes that often follow shifts in religious affiliation, suggesting that material motivations drive conversions. However, conversions are not always followed by improved economic outcomes, and even when they are, they may not be driven by material concerns. This project could shed light on the mechanisms involved in conversion processes by introducing a cultural evolutionary approach which focuses on the transmission pathways through which these ideas spread, and their interaction with human motivations and cultural norms.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": "12649", "attributes": { "award_id": "2222337", "title": "The Science and Mathematics Education Research Collaborative Postdoctoral Program", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "NSF Research Traineeship (NRT)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-10-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 11489, "first_name": "Scott", "last_name": "Franklin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 510, "ror": "", "name": "Rochester Institute of Tech", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 510, "ror": "", "name": "Rochester Institute of Tech", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will support a cohort of postdoctoral fellows within the Science and Mathematics Education Research Collaborative (SMERC), an interdisciplinary group of STEM education researchers from physics, biology, mathematics, chemistry, science communication, and engineering at the Rochester Institute of Technology (RIT). The postdoctoral fellows will work with mentors on a wide range of interdisciplinary research topics, and participate in professional development that includes research, classroom practice, and community involvement activities. The research and professional development opportunities are designed to help the postdoctoral fellows develop the knowledge and skills to conduct foundational research in STEM education. The interdisciplinary focus of the program will also help to bring together researchers from different disciplines, breaking down traditional academic silos and thus better preparing the fellows to conduct interdisciplinary STEM education research. A distinguishing feature of this postdoctoral training effort is its commitment to developing an interdisciplinary community of STEM education scholars. Research and professional development activities include opportunities for fellows to participate in research projects from a variety of single- and multi-disciplinary topics, to work with mentors from various disciplines, to gain knowledge of and skill with a variety of research methods (e.g., quantitative stochastic modeling and network analysis of large institutional data sets, and various qualitative methods), to participate in existing RIT affiliated programs that share SMERC’s core interdisciplinary philosophy, and to gain teaching knowledge and skills working with faculty instructors. The program also serves to build significant capacity for the RIT to develop into a regional hub of discipline-based education research.This project is funded by the NSF Research Traineeship (NRT) Program, which is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.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": 1391, "pages": 1424, "count": 14236 } } }