Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1404&sort=-approved
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However, achieving reliable healthcare artificial intelligence (AI) technology is challenging due to the scarce annotation problem caused by the cost of professional labeling, heterogeneity of human labels, and concerns over healthcare information privacy. To overcome this scarce annotation problem, there are currently two solutions. One solution is enabling multi-institutional collaboration to train a dedicated model. Another is obtaining multi-scale features from training a shareable model to fine-tuning multiple tasks. The first method involves huge computation and communication costs to train a dedicated model for only one type of task; while the second method is currently only applied to the autonomous car where a rich amount of data is collected and processed frequently on a single node. This project seeks to fill this gap by innovatively training multiple medical imaging tasks together with a cache mechanism to build an efficient and effective multi-institutional collaborative system. The goal of the proposed research is to design a multi-task learning model for medical imaging tasks, design a cache mechanism for the system to active relevant portion when interpreting a specific task, and develop a prototype distributed multi-task learning system for medical imaging to evaluate the model and system performance of the proposed solutions. Building a one-for-all collaboration system for medical imaging will constitute a significant technological breakthrough toward achieving practical AI in clinical practice. By facilitating the model sharing within and among institutions, the proposed system can address the scarce annotation problem and accelerates clinical detection, diagnosis, and treatment, to benefit healthcare professionals. Furthermore, as a general-purpose framework, the proposed system will also be deployed to other fields with similar application requirements, such as connected autonomous driving and other mobility systems on land, in the air, and at sea, where multiple nodes work as a unit for a series of tasks. From the education aspect, the proposed system will be developed as a basic experimentation platform for healthcare AI and can be easily transplanted to other scenarios, such as smart homes and smart transportation. The proposed system will be used for undergraduate and graduate education and research with the goal to inspire students' interests in edge intelligence, broaden participation in intelligent computing, networking, and systems, and enhance education diversity, inclusion, and equity.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": "12523", "attributes": { "award_id": "2245765", "title": "CRII: CNS: A Systematic Multi-Task Learning Framework for Improving Deep Learning Efficiency on Edge Platforms", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CSR-Computer Systems Research" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-06-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28454, "first_name": "Tianyun", "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": 287, "ror": "https://ror.org/002tx1f22", "name": "Cleveland State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Multi-task learning is a subfield of machine learning in which the data is trained with a shared model to solve different tasks simultaneously. Multi-task learning highly reduces the number of parameters in the machine learning models and thus reduces the computational and storage requirements. For example, there are multiple tasks to be done in real-time in self-driving cars, including object detection and depth estimation. If these tasks can be trained on a single model with shared parameters, the model size and the inference time can be highly reduced. This project aims to further compress the model used for multi-task learning as the model size of a single deep neural network is still a critical challenge to many computation systems, especially for edge platforms. This project proposes an approach to learn the difficulty of every task and maintain the performance of the most difficult task when compressing a multi-task learning model. It increases the potential in the compression rate with acceptable performance for all the tasks as the performance of the most difficult task needs to be guaranteed to provide a satisfactory user experience. This project also designs an efficient multi-task federated learning approach for edge platforms. It improves the convergence rate of multi-task federated learning and reduces the communication costs in every iteration. Finally, this project proposes to solve an algorithm-hardware co-design problem to maximize the implementation efficiency of the compressed multi-task DNN models on edge platforms.The files of compressed DNN models and the ideas on efficient DNN training and implementation may be useful to researchers who focus on improving the computation efficiency of DNN models on edge platforms and other hardware platforms.This project will involve undergraduate and graduate students in the research. The research achievements of this project will be incorporated into a current senior-level undergraduate course, a new planned advanced-level graduate course, and seminars for both undergraduate and graduate students. There are also planned research demonstrations during the workshops and summer camps for the K-12 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": "12524", "attributes": { "award_id": "2245849", "title": "CRII: CNS: Exploring Data and Model Sparsity in Deep Learning Systems using Graphs", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CSR-Computer Systems Research" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-06-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28455, "first_name": "Pradeep", "last_name": "Kumar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 414, "ror": "", "name": "College of William and Mary", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Due to the increase in the size of deep learning models, huge computation and memory resources are dedicated for their training, and deployment. To this regard, the proposal treats sparsity as a key primitive to reduce the requirement on the computational and memory resources without sacrificing the accuracy of the models. It seeks to investigate the fundamental understanding of the sparsity in both the data and model at the system-level using graph as a data-model. The preliminary study gathered a few system-level requirements and show that blindly adopting the best practices of sparse linear algebra and graph analytics systems fields, both of which are unrelated to deep learning, lead to many new system-level overheads for deep learning computation. The research proposal will lay down a better sparse data representation to improve the data locality, and will develop theory and systems to solve the workload imbalance problem in the computation involving sparse data and models. The proposal will result in reduction of memory consumption and will achieve faster computation. Both the goals of the proposal will also enable resource-constrained devices to be a part of the deep learning computation ecosystem.Deep learning has enabled use-cases that are being used for diverse societal goals including in the fields of medical science, defense, financial fraud detection, etc. By enabling such use-cases of more diverse datasets and models, both sparse, the proposal seeks to create better impact on the society by achieving faster computation at reduced memory usage, resulting in reduced cost for training and deployment of various deep learning models. The more immediate impact will be in the educational activities in the form of incorporating the developed learning tool(s) for classroom teaching, student projects, outreach to under-represented, minority and female students. Due to the strong interest from the industry in this field, students will be exposed to industry standard tools, as well get trained on end-to-end deep learning systems and data science both-- a key industry requirement that was also identified by the industry partners in an NSF Workshop.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": "12525", "attributes": { "award_id": "2327682", "title": "Work-Based Biotechnology Education from High School to Community College", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-05-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 2073, "first_name": "Ying-Tsu", "last_name": "Loh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2111, "ror": "", "name": "BAY AREA BIOSCIENCE EDUCATION COMMUNITY", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Biotechnology and related life sciences are among the fastest-growing industries in the nation, with a high demand for entry-level workers holding two-year or four-year college degrees. Because of the high density of biotechnology industries and research laboratories in the San Francisco Bay area, this region has high demand for biotechnology workers. However, the region has a shortage of trained entry-level biotechnology workers. Moreover, few individuals from African American and Hispanic communities pursue careers in biotechnology. Thus, a compelling need exists for opening new career paths in biotechnology for students from these communities, which have been disproportionately affected by the COVID-19 pandemic. To address this need, the project will support the City College of San Francisco’s Biotechnology program to work with local high schools to spark the interest of high school students in biotechnology. Students will have access to hands-on activities, field trips, and real-work experiences that include paid internships. These actions are designed to broaden participation of underrepresented groups in biotechnology and increase the capacity for biotechnology workforce development.The goal of this project is to address the biotechnology workforce shortage and lack of diversity by strengthening the pipeline of high school students from communities that are underrepresented in STEM into biotechnology studies at a community college. To accomplish this goal, the biotechnology faculty at City College of San Francisco will collaborate with teachers at local high schools with higher populations of the targeted students. High school students will visit the college during field trips and participate in carefully planned hands-on activities. In addition, high school students will gain valuable laboratory work skills during a paid summer internship. These students will receive training in leadership and peer mentoring skills and will be given the opportunity to work as classroom teaching assistants. The students will learn about careers in biotechnology and educational opportunities at the College. Lastly, under supervision of the faculty, City College of San Francisco students will develop outreach and instructional videos to supplement the instructional material for the College’s biotechnology courses and strengthen the high school student learning experience. The videos will be disseminated freely to high school and other educators. 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": "12526", "attributes": { "award_id": "2237743", "title": "CAREER: Unraveling the Role of the Varying Ocean Circulation in Climate Change", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Climate & Large-Scale Dynamics" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-05-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28456, "first_name": "Wei", "last_name": "Liu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Varying ocean circulations play an important role in climate change. These circulations are driven by surface winds and the changing buoyancy of the sea. Separating the effects of wind- and buoyancy-driven ocean circulations on climate change from the effects of other varying and interacting climate components is difficult. By isolating and quantify the effects that drive the ocean, the main goal of this project is to obtain an in-depth understanding of the role of varying ocean circulations in on-going and future climate changes. The work can help quantify the climate impacts of the ocean circulations (such as storm track shift and tropical rain-belt migration), thereby advancing climate predictions. Along with course developments and public outreach, the project will train young scientists at the University of California Riverside. The project will apply a novel ensemble-overriding method to multiple climate models to address several key questions: (1) What are the effects of wind- and buoyancy-driven ocean circulation changes under various anthropogenic forcings? (2) How to understand the coupled changes in extratropical atmospheric circulation and wind-driven gyre circulation? (3) How will Southern Ocean circulation change contribute to climate response to anthropogenic forcing? (4) How to understand the distinct changes and climate impacts of the simulated historical Atlantic meridional overturning circulations between the latest two generations of climate models? The activities will help train one postdoctoral scholar and one Ph.D. student. The investigator will incorporate project results into an undergraduate course and mentor undergraduate students in research at University of California Riverside, conduct workshops and give public lectures at Museum of Riverside, and conduct summer research program for Riverside Unified School District K-12 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": "12527", "attributes": { "award_id": "2332311", "title": "Collaborative Research: CEDAR: Searching for the Strongest Thermospheric Wind and Highest Temperature inside Strong Thermal Emission Velocity Enhancements", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "AERONOMY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-05-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28457, "first_name": "Ying", "last_name": "Zou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "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).Auroras are a spectacular display of the effects of Sun's energetic particles raining down and colliding with Earth’s atmosphere at high latitudes. These colorful displays are widely observed to have several typical shapes and colors. Recently, a ‘new’ type of aurora-like light at high latitudes has been observed. It often consists of a narrow purple arc with narrow green ‘picket-fence’ features. Its distinct shapes and colors are different from typical auroras. Scientists named it as STEVE (Strong Thermal Emission Velocity Enhancement). Because STEVE occurs at lower latitudes than typical auroras (called sub-auroral region), it is believed to be associated with a strong flow of charged particles in the ionosphere, a layer above our atmosphere with free-flowing ions and electrons. Research on STEVE will help us understand the interactions between the high and low latitude ionosphere and how solar energy impacts the entire Earth’s atmosphere system and space weather.As its name suggested, STEVE is interpreted to be due to the chemiluminescent reactions induced by heat generated by strong ion neutral interaction at large ion drift (~ 5000 m/s). To test the hypothesis of heat from strong ion-neutral interaction causing STEVE, both ion drift and thermospheric wind observations are necessary. The Swarm satellites have provided ion drift data in STEVE studies, but observations are missing for the thermospheric wind and temperature observations in the sub-auroral region. To obtain thermospheric temperature and wind observations for ascertaining the STEVE emission mechanism, this project will deploy a Fabry Perot Interferometer (FPI) at Athabasca University (54.60N, 113.64W, 61 MLAT), where an optical observatory is located and STEVE had been observed. In addition, a small color CCD all sky camera will be used to monitor the occurrence of STEVE and auroral activities and a dual-band GPS receiver will be used to monitor ionospheric Total Electron Content variations. The team plans to spend 20 nights/year to take all sky camera images in real time and steer the FPI sky scanner toward the STEVE when it is sighted. The real time monitoring is planned for substorm events following future solar active region events, which will be tracked from NOAA space weather services. The auroral images and FPI data will be studied in combination with the Swarm ion drift data to achieve a comparatively complete diagnosis of the ionosphere and thermosphere conditions. The project will address three topics: 1) Thermospheric conditions associated with STEVE and their effect on its emission; 2) SAPS effects on thermospheric winds; 3) Thermospheric wind's effect on the expansion of the substorm negative phase. Given the extreme ionospheric condition associated with STEVE, large thermospheric wind speed and high neutral temperature could be observed, which can change our perception of thermosphere-ionosphere interactions. The proposed Athabasca FPI can bridge the large spatial gap between the Resolute and Boulder FPI and allow the substorm effect from high to mid latitudes to be tracked. Since Athabasca is located at a latitude range that is strongly affected by the substorm negative phase, the results of this project will directly address a major concern for space weather forecast.Since STEVE was first observed by citizen scientists, this project will generate a greater public awareness of the aeronomy research and will stimulate future public engagement with science and technology. The project will foster diverse collaborations and establish a partnership between NCAR and a scientifically competitive institution in an EPSCoR state. The proposing team would make this project an opportunity to promote STEM education, increase the participation of women and underrepresented minorities in STEM.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": "12528", "attributes": { "award_id": "2247157", "title": "Fostering Communities of Practice Through Research and Peer Mentoring", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-05-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28458, "first_name": "Derrick", "last_name": "Swinton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2112, "ror": "https://ror.org/04wzzqn13", "name": "Kean University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to develop a national model for STEM education at Hispanic-Serving Institutions that engages all members of the university community. Central to this effort is the development of a “Scholars Academy,” a cohort-based program to support undergraduate students with peer mentoring, undergraduate research opportunities, and additional support. The academy is designed to improve undergraduate success, particularly for Hispanic students. The project will address a number of well documented challenges to undergraduate student success, including the fact that Hispanic STEM students persist in STEM at a lower rate than their non-Hispanic peers. Communities of Practice (CoPs) – groups of people who share a common concern and work and learn together – will be developed among students, faculty, and administrators to ensure longer term retention of students and create institutional transformation by sustaining best practices. Through this project, Kean will increase the number of faculty engaging in research with undergraduates, and who are also trained in culturally responsive mentoring. These practices are intended to increase student retention, job readiness, and improve participants' sense of belonging.Key to this Scholars Academy will be three CoPs fostered as part of the project activities. To foster these communities, activities will be implemented based on smaller programs already piloted in two academic units at Kean. The first such activity is to build a community of student scholars, and meet the needs of students during critical attrition points, by offering a STEM major-specific version of the \"Transition to Kean” first-year seminar. First- and second-year courses with high D, F, and withdrawal (DFW) rates will be targeted for supplemental instruction using peer-mentoring and peer-led team learning. Secondly, students will engage in small faculty-led research teams using the Computing Alliance for Hispanic Serving Institutions’ (CAHSI) Affinity Research Group model, with progress over multiple years and mentoring by senior-level students. A third activity involves implementing signature practices from CAHSI and Kean’s Center for Teaching and Learning to provide a variety of faculty professional development experiences aimed at enhancing advising and faculty pedagogy. A fourth and final activity is to form a team of faculty and administrators who will participate in an institutional transformation CoP that will help the project iteratively improve and institutionalize key project components. Project research will generate new knowledge on the effectiveness of the project's main interventions and what mechanisms supported the persistence and institutionalization of specific practices. The HSI Program aims to enhance undergraduate STEM education, broaden participation in STEM, and build capacity at HSIs. Achieving these aims, given the diverse nature and context of the HSIs, requires innovative approaches that incentivize institutional and community transformation and promote fundamental research (i) on engaged student learning, (ii) about what it takes to diversify and increase participation in STEM effectively, and (iii) that improves our understanding of how to build institutional capacity at HSIs that are supported by this program.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": "12529", "attributes": { "award_id": "2330988", "title": "GP-UP: Collaborative Research: Developing a diverse hydrology workforce through an undergraduate hydrological research experience in a coastal California watershed", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "IUSE" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-05-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28459, "first_name": "Amelia", "last_name": "Vankeuren", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1849, "ror": "", "name": "University Enterprises, Incorporated", "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 project will boost recruitment and retention of Sophomore and transfer students in hydrology and geoscience programs across three campuses of the California State University (CSU). Our program will engage with and support these students in the summer between their first and second year, or at the outset of their transfer, and provide pathways for them to succeed in hydrology and the geosciences. The CSU student body is ethnically, economically and academically diverse. Increasing participation of CSU students in hydrology and water resources science is critical to training a skilled workforce in this area, and therefore to securing California’s water future. The project offers a dual experience - a year-long learning community model where students work in small groups to complete research projects and gain transferable and subject-based research skills, supplemented by an immersive 10-day field experience at a hydrology research facility. During the field experience, students will contribute to cutting-edge research in coastal Californian watersheds, where rich habitat and summer baseflows sustain federally-protected salmonid fish species. Students who complete the program should have the motivation and skillset to pursue a geoscience career, and have strong resumes to apply to jobs, Masters programs and research internships in their home institutions or beyond as they reach their senior year.The program will recruit cohorts of students from San Diego State University, Sacramento State University, and Humboldt State University, all of which are minority serving institutions. The recruitment plan includes leveraging existing programs at each campus that engage students from diverse backgrounds in STEM disciplines. Students will participate in the experience in three stages: (1) Spring research preparation seminar series and cross-CSU webinars, (2) Intensive 10-day summer field experience with a cross-CSU cohort, and (3) Fall class for a sustained learning experience and presentation of results at a regional conference. Student research will be organized around three research themes: (1) Water held within and moving through the critical zone, (2) Water quality and aquatic habitat, and (3) Scaling up and human effects. Planned outcomes include development of research and professional skills, increased identities as members of the geoscience community, and increased student retention in the geosciences.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": "12530", "attributes": { "award_id": "2313518", "title": "CAREER: Multimodal Brain and Body Music Interfaces to Promote Entrainment, Connection, and Creative Science Education", "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": "2023-04-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28460, "first_name": "Grace", "last_name": "Leslie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 172, "ror": "", "name": "University of Colorado at Boulder", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Entrainment is a process in which people’s natural brain and body rhythms synchronize, through stimuli such as music, which may create feelings of connection and well-being. This project addresses entrainment by building multimodal signal mapping interfaces that mediate interpersonal connections by deriving music from brain and body rhythms. The investigator will integrate sensor hardware and signal processing software to stream live brain and body data, perform calculations to extract signal characteristics, and use this to drive sound synthesis. A series of music cognition and listening experiments study physiological, behavioral, and affective entrainment phenomena, which are expected to result, from a series of multimodal brain music interfaces. A use-case study, developed in consultation with doctors, connects mothers and infants, physically separated by distance, using the multimodal entrainment interface. Mother and infant hear music derived from each other’s heartbeats and breathing. This study investigates the entrainment created in their body rhythms, and maps health and well-being effects of the virtual connection environment. For researchers, doctors, and caretakers, multimodal brain music interfaces have the potential to expand our scientific understanding of music’s beneficial effects on the brain and body, which may lead to new health and well-being interventions for adults, children, and infants. This project will result in an open-source tool kit of accessible technologies and STEM learning modules to inspire educators and students to develop projects that further our understanding of brain and body signals. These learning modules will be integrated into a summer research experience--involving high school students and their teachers--in which authentic learning encourages students’ training in the scientific method through their natural interest in music. This project develops and evaluates an interface with new multimodal signal mapping technologies that translate neurophysiological signals (e.g., EEG, ECG, EDA, respiration) into musical sound to promote biological, behavioral, and affective synchrony between individuals and computers by: (1) engineering sonification techniques that perform real-time signal processing and algorithmic music generation for transforming physiological signals into music; (2) investigating the neuropsychological mechanisms that govern auditory neurostimulation and physiological entrainment by designing new rhythmic auditory neurophysiological sonification stimuli and measuring how the human body responds; and (3) designing and evaluating a use case that involves co-generating music for infants and their mothers with each other’s physiological data. Quantitative data will address synchronies in physiology, protocol analysis of video will address behavioral synchronies, and qualitative data will address experiences. These research activities will contribute to an overarching goal of discovering how using computing to pair music and physiology can function as a significant information channel in human-centered computing. One expected use of this channel is to promote human connection and well-being through entrainment.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": "12531", "attributes": { "award_id": "2229337", "title": "Collaborative Research: SHINE: Where Are Particles Accelerated in Coronal Jets?", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "SOLAR-TERRESTRIAL" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-04-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28461, "first_name": "Valeriy", "last_name": "Tenishev", "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": "Key questions remain regarding the source regions of impulsive solar energetic particles and their escape into the heliosphere. Understanding their origin will help in forecasting space weather and its impacts on spacecraft and instruments. This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal to enhance understanding of processes by which energy in the form of magnetic fields and particles are produced by the Sun and accelerated in interplanetary space. Graduate and undergraduate researchers will be supported. Further, a database of solar coronal-jet events will be created.The project is an observational and theoretical study of coronal jets to answer two science questions: (1) Where are electrons accelerated in active-region periphery jets? (2) How do flare-accelerated particles from active-region periphery jets escape into the heliosphere? The approach combines high-quality observations with state-of-the-art numerical simulations. The team will select and analyze a set of coronal jets at active-region peripheries from space-based and ground-based observatories, including the NSF-funded Expanded Owens Valley Solar Array. They will determine which types of jets are associated with impulsive solar energetic particle events, where the high-energy electrons are located both within and beyond the solar sources, and how these events evolve. Their magnetic topologies will be estimated by nonlinear force-free field extrapolations from magnetograms. Based on the data analysis results, they will perform simulations with initial conditions consistent with typical properties of the observed events. Postprocessing the simulation output with the particle-tracking code will reveal where electrons are energized, how their spectra evolve, and how these energetic particle escape.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": 1404, "pages": 1419, "count": 14184 } } }