Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1404&sort=-funder
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-funder", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1403&sort=-funder" }, "data": [ { "type": "Grant", "id": "14610", "attributes": { "award_id": "2427876", "title": "Conference: The 18th U.S.-Korea Forum on Nanotechnology: Sensors Related to Human Cognition and Sustainability in Semiconductor Manufacturing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Nanoscale Interactions Program" ], "program_reference_codes": [], "program_officials": [ { "id": 971, "first_name": "Nora", "last_name": "Savage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": null, "award_amount": 49740, "principal_investigator": { "id": 27026, "first_name": "Myung", "last_name": "Jhon", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [ { "id": 27024, "first_name": "Ahmed A", "last_name": "Busnaina", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 27025, "first_name": "Elias", "last_name": "Towe", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31279, "first_name": "In Hee", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31281, "first_name": "Bruno", "last_name": "Azeredo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Since 2003, the Forum on Nanotechnology between the U.S. and Korea has been held annually in alternating countries, except during the COVID-19 pandemic years. This joint forum facilitates networking between the research communities and agencies of both countries, enabling each side to exchange information and to explore opportunities for research collaboration in the field of nanotechnology. This year, the forum will address sensors related to human cognition and sustainability in semiconductor manufacturing. Two keynote speakers, eight senior presenters, and eight early-career presenters from the U.S. will be invited, and a roughly equal number of Korean presenters will participate. The organizing committee strives to emphasize the diversity of the early-career participants. The primary contribution of this forum is its ability to bring together a bi-national community of expert researchers who are working on the leading edge of sensors, sustainable electronics, and semiconductor manufacturing. The outcomes of this forum will lead to tangible milestones and vigorous research collaboration among researchers in both countries. Sustainability in semiconductor manufacturing will generate great economic and social impact in the U.S. Forum proceedings and findings for 2024 and previous events are available on Carnegie Mellon’s website.<br/><br/>Entering the fourth industrial revolution, which is characterized by a fusion of technologies that blur the lines between the physical, digital, and biological spheres, the rapid increase in data processed and stored requires emerging technologies related to human cognition. As a continuing effort from the fourteenth forum, sensors including wearables for human interface will be examined. Semiconductor technology is the most important thrust area in economic development, defense, and security for nations during the upcoming decade. To promote a paradigm shift for next-generation semiconductors, cost and performance gains, including advanced packing processes, in the continued pursuit of Moore’s law in the future will be examined. As chip sizes continue to decrease, novel environmental, health, and safety solutions should be reexamined. Developing novel devices using nanotechnology for the evaluation of chemistry for materials of high concern and environmental impacts during current and future semiconductor fabrication will be discussed. In semiconductor manufacturing, cost-effective processes using nanotechnology are needed to assure that wastewater effluents and air emissions have minimal environmental impact. Significant amounts of ultra-pure water and energy are required in the semiconductor manufacturing processes; thus, novel reduction, reuse, and recycling techniques for water in manufacturing and chemical mechanical planarization will be discussed.<br/><br/>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": "14611", "attributes": { "award_id": "2427937", "title": "Conference: International symposium on crystalline organic metals, superconductors, and magnets", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "DMREF" ], "program_reference_codes": [], "program_officials": [ { "id": 29924, "first_name": "Eugenia", "last_name": "Kharlampieva", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": null, "award_amount": 12600, "principal_investigator": { "id": 31282, "first_name": "Janice", "last_name": "Musfeldt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Non-technical Description: This award funds the international symposium on crystalline organic metals, superconductors, and magnets (ISCOM) to be held from September 22 - 27, 2024, in Anchorage, Alaska (USA). This workshop aims to advance the fundamental understanding of molecular and molecule-based materials, inspire new collaborations, and provide opportunities for the education and growth of a diverse group of scientists. A particular emphasis is on the role of predictive theory in developing the properties of molecular and molecule-based materials in the Materials Genome Initiative spirit. Traditionally, the ISCOM workshop rotates between Europe, Asia, and North America, and it has not been in the United States since 2005. One of the workshop tasks is to rebuild the community after the COVID era by focusing on contemporary research topics and placing a priority on the involvement of junior scientists as speakers. <br/><br/>Technical Description: The workshop focuses on molecular and molecule-based materials, including the interface between theory and experiment. This event intends to provide an opportunity to show off scientific accomplishments and create new partnerships and international collaborations to leverage complementary expertise in the field. A diverse group of US and international speakers is arranged for student tutorials and technical sessions. Presented topics include but not be limited to Superconductivity, Non-equilibrium phenomena, Magnetic and conducting metal-organic frameworks, Hydrogen and halogen bonding, Molecular ferroelectrics and multiferroics, Self-assembled heterostructures, Chemistry of new molecular materials, Light-induced properties, Spin liquids and frustration, glassy charge and spin states, Basic science for molecular devices, Spintronics, Quantum information, and Databases and machine learning. DMREF support is requested to partially cover costs for the conference venue to help reduce the registration fees.<br/><br/>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": "14613", "attributes": { "award_id": "2420964", "title": "EAGER: Private Blockchain-Enabled Federated Learning Framework for Distributed Manufacturing Networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "MSI-Manufacturing Systms Integ" ], "program_reference_codes": [], "program_officials": [ { "id": 31283, "first_name": "Janis", "last_name": "Terpenny", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": null, "award_amount": 299999, "principal_investigator": { "id": 31284, "first_name": "Thorsten", "last_name": "Wuest", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 385, "ror": "", "name": "West Virginia University Research Corporation", "address": "", "city": "", "state": "WV", "zip": "", "country": "United States", "approved": true }, "abstract": "In recent years, global manufacturing networks experienced a variety of shocks and disturbances including COVID-19. Thus, improving network resiliency, transparency, and cybersecurity have emerged as a national priority. Smart Manufacturing technologies such as Artificial Intelligence and Machine Learning show promise in achieving these objectives, yet struggle to materialize at the manufacturing network level. Particularly small and medium-sized manufacturers struggle in their adoption of these data-driven, value added technologies due to a lack of resources and incentives. Consequently, they cannot participate in many high-value manufacturing networks that often require certain technologies and data sharing. This EArly-concept Grant for Exploratory Research (EAGER) project supports research that intends to address this challenge through a Blockchain-enabled framework that leverages secure and private Federated Learning which meets the unique requirements of defense manufacturing networks. This framework enhances the availability and integrity of critical supplies, as well as strengthens and diversifies the defense industrial base. The project’s secure and privacy-preserving data sharing and collaboration mechanisms can be applied in various domains beyond manufacturing, such as healthcare, finance, and supply chain, empowering individuals and organizations to share data securely and collaborate effectively. The results have potential to transform industry, drive economic growth, foster innovation, and enhance societal well-being. <br/><br/>The project’s research problem stems from manufacturing networks’ inability to securely and efficiently exchange data and leverage network level Federated Learning. The project aims to increase the resiliency of distributed and dynamic manufacturing networks, specifically including small and medium-sized manufacturers, by providing access to a secure private Blockchain platform that enables decentralized, secure, and transparent communication channels. This enables manufacturing network level learning through Federated Learning while respecting data ownership and ensuring retention of competitive or controlled (raw) data and machine learning models. To achieve these goals, the project utilizes Federated Learning by integrating a private Blockchain to manage metadata, access controls, and model updates. Unlike existing approaches, the framework focuses on specific challenges and requirements of manufacturing networks. This means ensuring confidential data remains local under full control of the individual nodes while leveraging Blockchain for efficient coordination of the Federated Learning process as well as reducing overhead cost for smaller network participants that are resource constraint. The project advances the state-of-the-art in Federated Learning and Blockchain technology through efficient algorithms for model aggregation and coordination in the presence of heterogeneous data for manufacturing networks.<br/><br/>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": "14614", "attributes": { "award_id": "2402580", "title": "EAGER: IMPRESS-U Adaptive Infrastructure Recovery from Repeated Shocks through Resilience Stress Testing in Ukraine", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director", "International Research Collab" ], "program_reference_codes": [], "program_officials": [ { "id": 1546, "first_name": "Maija", "last_name": "Kukla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-01", "end_date": null, "award_amount": 298348, "principal_investigator": { "id": 31293, "first_name": "Rafael", "last_name": "Munoz-Carpena", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1635, "first_name": "Gregory A", "last_name": "Kiker", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, { "id": 31292, "first_name": "Ziynet Boz", "last_name": "Ozdemir", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "This IMPRESS-U project is jointly funded by NSF, Estonian Research Council (ETAG), Research Council of Lithuania (LMT), National Science Center of Poland (NCN), US National Academy of Sciences, and Office of Naval Research Global (DoD). The research will be performed in a multilateral international partnership that unites the University of Florida (US), G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine (PIMEE), Kiyv (Ukraine), National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (Ukraine), Institute of Theoretical & Applied Informatics, Gliwice (Poland), Mykolas Romeris University, Vilnius (Lithuania), and Tallinn University of Technology, Tallinn (Estonia). US portion of the collaborative effort will be co-funded by NSF OISE/OD and CISE/CNS. <br/><br/>NON-TECHNICAL SUMMARY <br/>This funding is awarded through an EAGER proposal, supporting the vital development of the Resilience-Recovery Under Attack (RRUA) framework. Originating in response to global disruptions like the COVID-19 pandemic, supply chain vulnerabilities, and geopolitical conflicts, the project holds significant importance. Focusing on Ukraine's digital services sector, which has faced persistent external shocks due to the ongoing conflict, this initiative is both timely and crucial. The primary objective is to establish connections between Ukrainian scientists and their counterparts in the West, mitigating the isolation caused by geopolitical tensions and fostering a collaborative research network dedicated to systemic resilience. Involving an international partnership comprising the USA, Ukraine, Poland, Estonia, and Lithuania, the project adopts an interdisciplinary approach, leveraging network science, resilience analytics, explainable AI, and digital twin technologies. Beyond enhancing Ukraine's infrastructural resilience, the project aspires to serve as a blueprint for global resilience strategies in analogous contexts.<br/>To achieve broader impact, the Dallas-Fort Worth airport will function as the initial RRUA testbed for co-development and training. The RRUA concept and methods will undergo testing in Poland, Estonia, and Lithuania's energy and communications infrastructure, culminating in their application to Ukraine's digital infrastructure. In addition to advancing research goals, this project is steadfast in its commitment to promoting inclusivity in science and engineering. It actively involves junior Ukrainian researchers, facilitating their integration into the global scientific community. Educational opportunities will be offered through digital platforms, workshops, and simulation games, aligning with EU-Ukraine events. These initiatives aim to provide a distinctive learning experience, nurturing a new generation of scientists equipped with the skills to address complex resilience challenges and aligning with the NSF's mission to advance national health, prosperity, and welfare.<br/><br/>TECHNICAL SUMMARY<br/>This NSF EAGER project award aims to advance resilience science by developing the Resilience-Recovery Under Attack (RRUA) framework, with a focus on the unique challenges faced by Ukraine's digital services sector. The RRUA framework introduces an innovative and comprehensive approach to resilience science, targeting the complex interdependencies of interconnected infrastructural systems subjected to dynamic threats and shocks. Hence, the project represents a significant leap from traditional resilience strategies that emphasize prevention, instead integrating recovery as a core component of the resilience paradigm. <br/> We aim to validate the hypothesis that the recovery and resilience of systems under threats and system response stages to diverse shocks can be quantified via stress-testing of interconnected networks representing their systemic functions. We aim to provide novel insights into the lifecycle of resilience under external shocks during acute and persistent shocks, and in particular quantify the key controllers of each of the resilience-response phases to inform efficient recovery interventions. By employing a multi-faceted methodology combining network science, resilience analytics, explainable AI (xAI), and digital twin technologies, the project seeks to redefine systemic recovery modeling and adaptation of interconnected infrastructure across Ukraine, benefiting from the shared knowledge of our proposed international partnership with the USA, Ukraine, Poland, Estonia, and Lithuania. The project utilizes a three-pronged approach: refining RRUA using data-rich analyses at a US-based international airport, testing concepts and methods in Poland, Estonia and Lithuania testbeds, including human behavior components of vulnerability, and subsequently integrating RRUA within Ukraine's cyber and energy infrastructure systems in the presence of dynamic threats and variable data. Success could revolutionize Ukraine's prospects for recovery, positioning it as a global example for resilience strategies. Our research design will collaboratively explore how to operationalize our RRUA framework across varied settings.<br/><br/>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": "14615", "attributes": { "award_id": "2345349", "title": "HSI Pilot Project: Enhancing STEM Participation and Attainment at a Rural, Hispanic-Serving Institution", "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": [ { "id": 28717, "first_name": "Sonal", "last_name": "Dekhane", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-01", "end_date": null, "award_amount": 299999, "principal_investigator": { "id": 31297, "first_name": "James", "last_name": "Hawker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31294, "first_name": "Justin C", "last_name": "Ortagus", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31295, "first_name": "Lindsay B", "last_name": "Lynch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31296, "first_name": "Daniel", "last_name": "Sanches", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2482, "ror": "https://ror.org/05ta7wm04", "name": "South Florida State College", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Improving Undergraduate STEM Education: Hispanic Serving Institutions (HSI Program), this Track 1 project aims to improve STEM participation and baccalaureate transfer for students at a rural HSI community college. Despite a rise in Hispanic student enrollment and graduation rates over the past few decades, Hispanic students remain underrepresented in STEM degrees and the STEM workforce. At rural HSI community colleges, many Hispanic students are unable to access required science lab coursework or engage in highly valuable undergraduate research experiences in STEM disciplines due to location constraints. Online courses in community college settings have the potential to increase access, persistence, and degree completion by removing location constraints and allowing students to continue to make progress toward their degree. The COVID-19 pandemic revealed that many under-resourced institutions, such as rural HSI community colleges, faced major challenges when seeking to deliver high-quality online laboratory science courses. Similar capacity constraints limit Hispanic students' undergraduate research opportunity at rural HSI community colleges. This research will contribute to the growing body of literature on the role that online instruction and undergraduate research experiences can play in advancing equity in STEM opportunities and outcomes via evidence-based best practices designed to build stronger STEM pathways and identities among Hispanic students at a rural HSI community college.<br/><br/>The aims of this project are to (1) increase access to STEM for rural students at and HSI and (2) improve degree attainment and transfer opportunities for HSI students pursuing STEM education. To address these two areas for improvement, SFSC will partner with the University of Florida Institute of Higher Education (UF IHE) to implement evidence-based best practices that increase access to required science courses and to undergraduate research experiences. These interventions will include high-quality online courses, innovative virtual science labs, and expanded undergraduate research experiences. Expected project outcomes include increased student enrollment in laboratory science courses, increased persistence of students in STEM majors, and increased participation of students in undergraduate research experiences. The collaborative research team from SFSC and UF IHE will explore the project's influence on students' STEM course participation, success, and persistence in their major. The project team will also examine faculty records for participation in undergraduate research experiences and conduct semi-structured interviews to better understand students' engagement and experiences with intervention components. Following an independent external evaluation, results of this work will be broadly disseminated at the state, regional, and national levels, including Florida College System institutions, professional associations, higher education research networks, and peer-reviewed journals focused on higher education and STEM education. To ensure broad dissemination, the project team will also create a dedicated project website and promote deliverables through the UF IHE website. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims.<br/><br/>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": "14616", "attributes": { "award_id": "2304278", "title": "SBIR Phase II: An immersive virtual reality platform for remote physical therapy and monitoring", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [ { "id": 806, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-01", "end_date": null, "award_amount": 1000000, "principal_investigator": { "id": 31298, "first_name": "Aviv", "last_name": "Elor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2483, "ror": "", "name": "IMMERGO LLC", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II<br/>project concerns greater affordability, accessibility, and accuracy of physical therapy for patients and<br/>therapists. The product to be commercialized will contribute to developing innovation around immersive<br/>telehealth experiences, exploring the future of work for physical rehabilitation in the metaverse,<br/>establishing standards in embodied telehealth, and grounding novel scientific research methods within VR<br/>for healthcare from a California-based startup with the goal of increasing access to care. Establishing a<br/>remote immersive virtual platform will provide a means for in-patient success metrics and full-body virtual<br/>guidance. Patient throughput potentially could be doubled through remote visits in virtual environments<br/>and automated physical health documentation. The platform will be designed with accessibility in mind<br/>with patients from \"medical deserts,\" where patient care is significantly limited by hospital capacity,<br/>physical distance, doctors per population, and cost. Remote physical rehabilitation tools and predictive<br/>physical therapy analytics will benefit patients without adequate insurance coverage. This technology<br/>could lower hospital visits, enable clinics to remain open during future pandemic periods, decrease costs<br/>for patients and clinics alike, and begin detecting physical health needs earlier to help manage the pace<br/>of recovery.<br/><br/>The proposed project aims to expand a novel physical rehabilitation telehealth solution through the<br/>continued research of an instrumented and gamified immersive virtual reality platform for physical therapy<br/>and healthcare monitoring. This technology addresses the shortcomings of widely used telehealth platforms (often videoconferencing) where therapists find it difficult to perform common evaluations<br/>such as movement abilities and balance coordination tests. The solution will expand upon an embodied<br/>telehealth platform with 3D virtual avatars and predictive AI tools to assess user biomechanics in real-time<br/>extending to full-body assessment while providing normative assessment metrics, creating a goal<br/>standard for remote physical therapy care. The development method will continue to utilize user-centered<br/>design with a panel of therapists to ensure accessibility and usability of the prototype systems by their<br/>relevant stakeholders. Such research will incorporate predictive biomechanical analysis to increase the<br/>reliability and repeatability of physical therapy measures and exercise programs for remote monitoring<br/>at the clinic or the patient’s home. Iterative prototyping with user experience will be conducted to<br/>establish in-patient success metrics and full-body virtual assessment. This innovation will enable greater<br/>affordability, usability, and effectiveness of physical therapy for patients and therapists.<br/><br/>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": "14617", "attributes": { "award_id": "2334897", "title": "Collaborative Research:CIF:Small:Fisher-Inspired Approach to Quickest Change Detection for Score-Based Models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Comm & Information Foundations" ], "program_reference_codes": [], "program_officials": [ { "id": 28975, "first_name": "James", "last_name": "Fowler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-01", "end_date": null, "award_amount": 300000, "principal_investigator": { "id": 31299, "first_name": "TAPOSH", "last_name": "BANERJEE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 272, "ror": "https://ror.org/01an3r305", "name": "University of Pittsburgh", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Detecting abrupt changes in the underlying statistical characteristics of online data streams is an important problem commonly encountered in many science and engineering applications. Examples include anomaly detection using video streams, line-outage detection in power grids, onset detection of a pandemic, and detection of cyber-attacks. While traditional techniques assume that probability distributions for both before and after the change are known or can be found, this assumption is unrealistic in most scenarios of practical interest. As an alternative to such traditional change-detection approaches, this project considers the use of deep neural networks to effectuate change detection. However, rather than attempting to learn probability distributions directly, the project leverages the recently-demonstrated ability of deep neural networks to learn the \"score\" (i.e., the gradient of logarithm of the probability density) from the data and aims to develop score-based algorithms for change detection. These scores can be learned for a large class of high-dimensional data models using modern tools of artificial intelligence and rendering the developed algorithms applicable to a broad class of change-detection problems. Fundamental mathematical theories will be developed in the project to establish the efficacy and efficiency of the proposed methods, and the developed algorithms will be validated on several publicly available machine-learning and anomaly-detection datasets. Broader-impact aspects of the project include providing algorithms to the wider community for solving change- and anomaly-detection problems across multiple, disparate fields as well as activities centered on integrating research into graduate coursework and providing opportunities for underrepresented students to participate in the project.<br/> <br/>The algorithms developed in the project will be based on the score of the data; this score can be explicitly derived for known unnormalized models or can be learned using score matching using an artificial neural network, and developed algorithms will be optimized to detect the changes with the minimum possible delay while avoiding false alarms. The project is divided into four technical thrusts. The first thrust will develop the fundamental theory for score-based quickest change detection for independent and identically distributed single-stream data under Bayesian, generalized Bayesian, and minimax problem formulations. While the performance of classical change-detection methods depends on the Kullback-Leibler distance between the distributions before and after the change, it will be established that the performance of the score-based methods depends on the Fisher distance between distributions. The second thrust will develop robust methods for detecting changes under modeling uncertainty, using the Fisher distance between the elements of the uncertainty classes. The third thrust will define the notion of scores for dependent data sequences and obtain optimal algorithms for detecting changes, with the scores in this case being based on the gradient of the logarithm of the conditional densities. The fourth and final thrust will develop algorithms for distributed change detection wherein multiple agents may have partial knowledge of the distributions and may only communicate with their neighbors in a geographical area. <br/><br/>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": "14619", "attributes": { "award_id": "2334898", "title": "Collaborative Research:CIF:Small:Fisher-Inspired Approach to Quickest Change Detection for Score-Based Models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Comm & Information Foundations" ], "program_reference_codes": [], "program_officials": [ { "id": 28975, "first_name": "James", "last_name": "Fowler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-01", "end_date": null, "award_amount": 298988, "principal_investigator": { "id": 31303, "first_name": "Vahid", "last_name": "Tarokh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Detecting abrupt changes in the underlying statistical characteristics of online data streams is an important problem commonly encountered in many science and engineering applications. Examples include anomaly detection using video streams, line-outage detection in power grids, onset detection of a pandemic, and detection of cyber-attacks. While traditional techniques assume that probability distributions for both before and after the change are known or can be found, this assumption is unrealistic in most scenarios of practical interest. As an alternative to such traditional change-detection approaches, this project considers the use of deep neural networks to effectuate change detection. However, rather than attempting to learn probability distributions directly, the project leverages the recently-demonstrated ability of deep neural networks to learn the \"score\" (i.e., the gradient of logarithm of the probability density) from the data and aims to develop score-based algorithms for change detection. These scores can be learned for a large class of high-dimensional data models using modern tools of artificial intelligence and rendering the developed algorithms applicable to a broad class of change-detection problems. Fundamental mathematical theories will be developed in the project to establish the efficacy and efficiency of the proposed methods, and the developed algorithms will be validated on several publicly available machine-learning and anomaly-detection datasets. Broader-impact aspects of the project include providing algorithms to the wider community for solving change- and anomaly-detection problems across multiple, disparate fields as well as activities centered on integrating research into graduate coursework and providing opportunities for underrepresented students to participate in the project.<br/> <br/>The algorithms developed in the project will be based on the score of the data; this score can be explicitly derived for known unnormalized models or can be learned using score matching using an artificial neural network, and developed algorithms will be optimized to detect the changes with the minimum possible delay while avoiding false alarms. The project is divided into four technical thrusts. The first thrust will develop the fundamental theory for score-based quickest change detection for independent and identically distributed single-stream data under Bayesian, generalized Bayesian, and minimax problem formulations. While the performance of classical change-detection methods depends on the Kullback-Leibler distance between the distributions before and after the change, it will be established that the performance of the score-based methods depends on the Fisher distance between distributions. The second thrust will develop robust methods for detecting changes under modeling uncertainty, using the Fisher distance between the elements of the uncertainty classes. The third thrust will define the notion of scores for dependent data sequences and obtain optimal algorithms for detecting changes, with the scores in this case being based on the gradient of the logarithm of the conditional densities. The fourth and final thrust will develop algorithms for distributed change detection wherein multiple agents may have partial knowledge of the distributions and may only communicate with their neighbors in a geographical area. <br/><br/>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": "14620", "attributes": { "award_id": "2346713", "title": "CC* CIRA: Bridging the Digital Chasm HPC for ALL", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Campus Cyberinfrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 2638, "first_name": "Kevin", "last_name": "Thompson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-01", "end_date": null, "award_amount": 200000, "principal_investigator": { "id": 31307, "first_name": "Michael", "last_name": "Kirby", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31305, "first_name": "Kelly C", "last_name": "Wrighton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31306, "first_name": "Candace L", "last_name": "Ramsey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "This two-year planning effort develops an inclusive vision for sustainable High-Performance Computing (HPC) at Colorado State University to support knowledge discovery in the era of big data. To accomplish this, a series of campus workshops and planning sessions are being held to foster engagement with stakeholders, as well as broaden participation in data-driven knowledge discovery. These activities include planning the development of training and educational materials to enhance accessibility of data-intensive computing to the campus community, addressing the integration of new parallel computing technologies with faculty and student research, and strengthening computing partnerships both regionally and nationally.<br/><br/>In conjunction with these user targeted activities, there is a simultaneous effort to update the university cyber-infrastructure plan including addressing the transmission, storage, and hosting of large data sets. An overarching goal of this planning phase is the development of a self-sustaining infrastructure that enables the campus community to cultivate expertise and contribute to workforce development in HPC, an area of national need. The broader impact of fostering a culture of high-performance computing and data driven discovery at CSU is the enhanced ability of faculty, students and graduates to make fundamental discoveries that improve society, e.g., predictive analytics for pandemic preparedness, climate and weather prediction, food supply through digital soil and crop sciences.<br/><br/>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": "14621", "attributes": { "award_id": "2347150", "title": "ERI: Realistic Drone Integration in Rural Healthcare Supply Chains", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "OE Operations Engineering" ], "program_reference_codes": [], "program_officials": [ { "id": 31308, "first_name": "Reha", "last_name": "Uzsoy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-01", "end_date": null, "award_amount": 199977, "principal_investigator": { "id": 31309, "first_name": "Shakiba", "last_name": "Enayati", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 374, "ror": "", "name": "University of Missouri-Saint Louis", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "This Engineering Research Initiation (ERI) award supports research enabling the integration of drone technology into healthcare logistics to address the challenges of access to medical supplies in rural areas. Rural communities are hindered by limited infrastructure, vast distances, rugged terrain and severe weather conditions, which significantly impact the timely and efficient delivery of essential healthcare supplies. Despite the recognized potential of drones to enhance healthcare delivery in such settings, optimizing drone operations considering practical constraints such as environmental factors (e.g., weather and wind conditions) and their impact on operational performance (e.g., range and payload) has proven challenging. This award addresses this need by developing and analyzing mathematical models to support drone integration into multi-modal transportation networks, considering the uncertainties of weather conditions and the specific needs of each delivery. This initiative aims not only to enhance healthcare access in rural areas but also to advance the state of the art in healthcare logistics, contribute to educational and diversity efforts in STEM, and ultimately support the national health and prosperity by ensuring efficient and equitable healthcare access across all communities.<br/><br/>This research will develop a decision-support tool for improving rural healthcare logistics through strategic drone use given variable weather and operational conditions. Probabilistic energy consumption modeling will allow the incorporation of uncertainties in drone performance into a robust optimization model that designs the logistic network under worst-case scenarios while also optimizing reliable operations to facilitate collaboration among multiple transportation modes, including drones. Innovative solution algorithms will be developed to handle complexities due to time-sensitive demand, and simulation experiments will be conducted to verify and validate the effectiveness of the drone-based delivery system. The simulation will be designed using both synthetic data that mimics rural landscapes and real-world data related to pandemic supply chains. Numerical experiments will evaluate the system's overall effectiveness by comparing scenarios with and without drone use, examining the trade-offs between operational efficiency and equitable distribution of medical supplies.<br/><br/>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": 1424, "count": 14236 } } }