{"response":{"award":[{"abstractText":"The U.S. electric power system is undergoing a rapid transformation driven by inverter-based resources, distributed energy technologies, and emerging artificial intelligence-driven electricity demand. Achieving the scientific and societal benefits of this transformation requires a research workforce that integrates power engineering with advanced cyberinfrastructure, scalable computing, data-driven modeling, and trustworthy artificial intelligence. However, these capabilities remain unevenly distributed across institutions and are not yet systematically embedded in power engineering education. This CyberTraining Implementation project, PowerCyber, addresses this gap by scaling a successful Pilot effort into a national training and community-building program for artificial intelligence-enabled power grid research. The project prepares faculty, postdoctoral researchers, and graduate students to adopt, teach, and extend modern cyberinfrastructure methods through hands-on training, reusable instructional materials, open-source computational environments, and sustained community engagement. By combining a train-the-trainer model with research-ready training pathways, PowerCyber amplifies its impact beyond direct participants and accelerates curriculum adoption across the broader power engineering community. The project strengthens the national cyberinfrastructure workforce, broadens access to advanced computational training, and supports more reproducible, scalable, and trustworthy research for future electric grids. These outcomes will help prepare engineers and researchers to build a cleaner, more reliable, and more resilient energy system.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"NORTH CAROLINA STATE UNIVERSITY","awardeeAddress":"2601 WOLF VILLAGE WAY","awardeeCity":"RALEIGH","awardeeCountryCode":"US","awardeeDistrict":"02","awardeeDistrictCode":"NC02","awardeeName":"North Carolina State University","awardeePhone":"9195152444","awardeeStateCode":"NC","awardeeZipCode":"276950001","cfdaNumber":"47.070","date":"07/28/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"300000","expDate":"12/31/2029","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource, CyberTraining - Training-based","fundsObligated":["FY 2026 = $300,000.00"],"fundsObligatedAmt":"300000","histAwd":"false","id":"2612493","initAmendmentDate":"07/28/2026","latestAmendmentDate":"07/28/2026","managingPec":"723100","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Hantao Cui","perfAddress":"2601 WOLF VILLAGE WAY","perfCity":"RALEIGH","perfCountryCode":"US","perfDistrict":"02","perfDistrictCode":"NC02","perfLocation":"North Carolina State University","perfStateCode":"NC","perfZipCode":"276950001","pi":["Hantao Cui hcui9@ncsu.edu"],"piEmail":"hcui9@ncsu.edu","piFirstName":"Hantao","piId":"270048852","piLastName":"Cui","poEmail":"dmassey@nsf.gov","poName":"Daniel F. Massey","poPhone":"7032925147","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00, 044Y00","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"01/01/2027","title":"Collaborative Research: CyberTraining: Implementation: Small: PowerCyber: Scalable Workforce Development for AI-Enabled Power Grids","transType":"Standard Grant","ueiNumber":"U3NVH931QJJ3"},{"abstractText":"The U.S. electric power system is undergoing a rapid transformation driven by inverter-based resources, distributed energy technologies, and emerging artificial intelligence-driven electricity demand. Achieving the scientific and societal benefits of this transformation requires a research workforce that integrates power engineering with advanced cyberinfrastructure, scalable computing, data-driven modeling, and trustworthy artificial intelligence. However, these capabilities remain unevenly distributed across institutions and are not yet systematically embedded in power engineering education. This CyberTraining Implementation project, PowerCyber, addresses this gap by scaling a successful Pilot effort into a national training and community-building program for artificial intelligence-enabled power grid research. The project prepares faculty, postdoctoral researchers, and graduate students to adopt, teach, and extend modern cyberinfrastructure methods through hands-on training, reusable instructional materials, open-source computational environments, and sustained community engagement. By combining a train-the-trainer model with research-ready training pathways, PowerCyber amplifies its impact beyond direct participants and accelerates curriculum adoption across the broader power engineering community. The project strengthens the national cyberinfrastructure workforce, broadens access to advanced computational training, and supports more reproducible, scalable, and trustworthy research for future electric grids. These outcomes will help prepare engineers and researchers to build a cleaner, more reliable, and more resilient energy system.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF SOUTH CAROLINA","awardeeAddress":"1600 HAMPTON ST","awardeeCity":"COLUMBIA","awardeeCountryCode":"US","awardeeDistrict":"06","awardeeDistrictCode":"SC06","awardeeName":"University of South Carolina at Columbia","awardeePhone":"8037777093","awardeeStateCode":"SC","awardeeZipCode":"292083403","cfdaNumber":"47.070","date":"07/28/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"200000","expDate":"12/31/2029","fundAgencyCode":"4900","fundProgramName":"CyberTraining - Training-based, NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $200,000.00"],"fundsObligatedAmt":"200000","histAwd":"false","id":"2612494","initAmendmentDate":"07/28/2026","latestAmendmentDate":"07/28/2026","managingPec":"723100","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"Q93ZDA59ZAR5","pdPIName":"Xin Fang","perfAddress":"1600 HAMPTON ST","perfCity":"COLUMBIA","perfCountryCode":"US","perfDistrict":"06","perfDistrictCode":"SC06","perfLocation":"University of South Carolina at Columbia","perfStateCode":"SC","perfZipCode":"292083403","pi":["Xin Fang fangxin@sc.edu"],"piEmail":"fangxin@sc.edu","piFirstName":"Xin","piId":"270086198","piLastName":"Fang","poEmail":"dmassey@nsf.gov","poName":"Daniel F. Massey","poPhone":"7032925147","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"044Y00, 296Y00","program":"Artificial Intelligence (AI), EXP PROG TO STIM COMP RES","progRefCode":"075Z, 9150","publicAccessMandate":"1","startDate":"01/01/2027","title":"Collaborative Research: CyberTraining: Implementation: Small: PowerCyber: Scalable Workforce Development for AI-Enabled Power Grids","transType":"Standard Grant","ueiNumber":"J22LNTMEDP73"},{"abstractText":"Artificial intelligence is rapidly changing how scientific research, education, and the workforce operate. However, many students and early-career researchers lack opportunities to learn how to use these technologies effectively, responsibly, and critically within their own fields. This project addresses that challenge by creating educational experiences that prepare undergraduate students to work with advanced artificial intelligence tools in research settings. By bringing together students from computing and social science disciplines, the project will help develop a workforce that can apply emerging technologies to real-world scientific and societal problems while understanding their limitations and risks. The project supports the national interest by strengthening the future research workforce, expanding access to advanced technological capabilities, promoting interdisciplinary collaboration, and improving the ability of researchers to use artificial intelligence in ways that are trustworthy, ethical, and beneficial to society.\r\n\r\nThis project will develop, implement, and evaluate a scalable set of interdisciplinary educational modules that integrate artificial intelligence literacy, applied machine learning skills, and responsible use of generative artificial intelligence tools. The project will provide modular instruction, hands-on practicum activities, and collaborative research experiences that pair computer science and social science students in data-intensive research tasks such as qualitative data coding, thematic analysis, and large-scale information processing. Assessment activities will examine how students develop technical competencies, evaluate artificial intelligence tools, and integrate domain knowledge with computational methods. Project outcomes will include the creation and delivery of evidence-based instructional practices, curriculum resources, and a replicable model for interdisciplinary artificial intelligence workforce development that can be adapted across multiple scientific disciplines and engineering fields.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"PURDUE UNIVERSITY","awardeeAddress":"2550 NORTHWESTERN AVE # 1100","awardeeCity":"WEST LAFAYETTE","awardeeCountryCode":"US","awardeeDistrict":"04","awardeeDistrictCode":"IN04","awardeeName":"Purdue University","awardeePhone":"7654941055","awardeeStateCode":"IN","awardeeZipCode":"47906","cfdaNumber":"47.070","coPDPI":["Daniel S Schiff dschiff@purdue.edu"],"date":"07/29/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"299897","expDate":"11/30/2028","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $299,897.00"],"fundsObligatedAmt":"299897","histAwd":"false","id":"2612295","initAmendmentDate":"07/29/2026","latestAmendmentDate":"07/29/2026","managingPec":"296Y00","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"YRXVL4JYCEF5","pdPIName":"Dan Goldwasser","perfAddress":"2550 NORTHWESTERN AVE # 1100","perfCity":"WEST LAFAYETTE","perfCountryCode":"US","perfDistrict":"04","perfDistrictCode":"IN04","perfLocation":"Purdue University","perfStateCode":"IN","perfZipCode":"47906","pi":["Dan Goldwasser dgoldwas@purdue.edu"],"piEmail":"dgoldwas@purdue.edu","piFirstName":"Dan","piId":"269952157","piLastName":"Goldwasser","poEmail":"shabagch@nsf.gov","poName":"Sharmistha Bagchi-Sen","poPhone":"7032928104","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), CI Training and Professionals, CYBERINFRASTRUCTURE/SCIENCE","progRefCode":"075Z, 122Z, 7569","publicAccessMandate":"1","startDate":"12/01/2026","title":"Cybertraining: Pilot: Building Cross-Disciplinary AI Literacy and Skill-Set","transType":"Standard Grant","ueiNumber":"YRXVL4JYCEF5"},{"abstractText":"The National Artificial Intelligence Research Resource (NAIRR) is a pioneering public-private initiative to catalyze a competitive national artificial intelligence (AI) ecosystem for discovery and innovation. The NAIRR connects U.S. researchers and educators to the most advanced public and private-sector computational and data platforms, datasets, software, AI models, and technological expertise necessary to accelerate AI-driven discovery and innovation. The NAIRR Operations Center (NAIRR-OC) serves as the community-based support organization responsible for the day-to-day coordination, operations, and development activities that implement the NAIRR vision to accelerate AI innovation and national competitiveness. \r\n\r\nThe NAIRR-OC delivers a production-scale capability that builds on the foundation of the NAIRR Pilot and decades of cyberinfrastructure leadership at the San Diego Supercomputer Center and the Texas Advanced Computing Center. Central to the project is an innovative framework that combines AI-enabled operations, data-informed decision-making, and the connective power of the nation’s multi-sector ecosystem spanning academic institutions, federal agencies, and private-sector partners to deliver scalable and adaptive national operations that continuously evolve as technologies and user needs change. The NAIRR-OC serves as the primary gateway through which a broad national community gains coordinated access to AI compute, data, models, and tools through flexible engagement pathways suited to any level of expertise. By addressing fragmentation across the AI ecosystem and integrating advanced resources with communication, support, and training, the NAIRR-OC enables AI-driven discovery at an unmatched speed and scale. This capability equips the nation with the infrastructure to embed AI into research, teaching, and workforce preparation while strengthening U.S. global competitiveness in the AI era.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF CALIFORNIA, SAN DIEGO","awardeeAddress":"9500 GILMAN DR","awardeeCity":"LA JOLLA","awardeeCountryCode":"US","awardeeDistrict":"50","awardeeDistrictCode":"CA50","awardeeName":"University of California-San Diego","awardeePhone":"8585344896","awardeeStateCode":"CA","awardeeZipCode":"920930021","cfdaNumber":"47.070","coPDPI":["Maytal Dahan maytal@tacc.utexas.edu","Subhashini Sivagnanam sivagnan@sdsc.edu","Mahidhar Tatineni Mahidhar@sdsc.edu","Ashley E Atkins asatkins@ucsd.edu"],"date":"08/31/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"35000000","expDate":"09/30/2031","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $7,000,000.00"],"fundsObligatedAmt":"7000000","histAwd":"false","id":"2616251","initAmendmentDate":"08/31/2026","latestAmendmentDate":"08/31/2026","managingPec":"296Y00","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Frank Wuerthwein","perfAddress":"9500 GILMAN DR","perfCity":"LA JOLLA","perfCountryCode":"US","perfDistrict":"50","perfDistrictCode":"CA50","perfLocation":"University of California-San Diego","perfStateCode":"CA","perfZipCode":"920930505","pi":["Frank Wuerthwein fkw@ucsd.edu"],"piEmail":"fkw@ucsd.edu","piFirstName":"Frank","piId":"269766199","piLastName":"Wuerthwein","poEmail":"alsuarez@nsf.gov","poName":"Alejandro Suarez","poPhone":"7032927092","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT","01002728DB NSF RESEARCH & RELATED ACTIVIT","01002829DB NSF RESEARCH & RELATED ACTIVIT","01002930DB NSF RESEARCH & RELATED ACTIVIT","01003031DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"10/01/2026","title":"NAIRR-OC: Advancing AI Discovery and Innovation for the Nation","transType":"Cooperative Agreement","ueiNumber":"UYTTZT6G9DT1"},{"abstractText":"Artificial intelligence (AI) has become essential to scientific discovery, yet its rapid adoption introduces risks that traditional cybersecurity controls cannot detect. Conventional security tools cannot tell whether an AI model has been tampered with so that it runs normally but still produces wrong scientific answers. The objective of VERITAS is to establish AI Assurance as a function that is distinct from, but complementary to, conventional cybersecurity. \r\n\r\nThe project addresses this through three coordinated tasks. First, through introduction of standardized documentation of model cards and dataset datasheets for substantial GPU allocations on national computing resources. Second, through deployment of an AI Assurance Engineer to inspect AI projects for hidden weaknesses, supported by an expert \"red team\" which safely probes systems the way an attacker might. Finally, through hands-on challenges through the National Data Platform Education Hub that train students nationwide to spot these problems themselves. The results will include validated AI workflows, a catalog of scientific-AI attack patterns and mitigations, and a trained AI-security workforce. By building these protections into the research infrastructure scientists already use, VERITAS makes AI-driven science more trustworthy and reliable for the nation.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF ILLINOIS","awardeeAddress":"506 S WRIGHT ST","awardeeCity":"URBANA","awardeeCountryCode":"US","awardeeDistrict":"13","awardeeDistrictCode":"IL13","awardeeName":"University of Illinois at Urbana-Champaign","awardeePhone":"2173332187","awardeeStateCode":"IL","awardeeZipCode":"618013620","cfdaNumber":"47.070","coPDPI":["Ilkay Altintas altintas@sdsc.edu","Bo Li bol@uchicago.edu"],"date":"07/20/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"896601","expDate":"09/30/2029","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $896,601.00"],"fundsObligatedAmt":"896601","histAwd":"false","id":"2613693","initAmendmentDate":"07/20/2026","latestAmendmentDate":"07/20/2026","managingPec":"802700","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"V2PHZ2CSCH63","pdPIName":"Anita Nikolich","perfAddress":"506 S WRIGHT ST","perfCity":"URBANA","perfCountryCode":"US","perfDistrict":"13","perfDistrictCode":"IL13","perfLocation":"University of Illinois Urbana-Champaign","perfStateCode":"IL","perfZipCode":"618013620","pi":["Anita Nikolich anitan@illinois.edu"],"piEmail":"anitan@illinois.edu","piFirstName":"Anita","piId":"270025975","piLastName":"Nikolich","poEmail":"dmassey@nsf.gov","poName":"Daniel F. Massey","poPhone":"7032925147","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"10/01/2026","title":"CICI: IPAAI: VERITAS - VERified Infrastructure for Trustworthy AI in Science","transType":"Standard Grant","ueiNumber":"Y8CWNJRCNN91"},{"abstractText":"Since the first direct observation of gravitational waves in 2015, a new field of astronomy has fundamentally changed how the universe can be explored. A gravitational wave is a ripple in spacetime produced when two extremely dense objects, such as black holes or neutron stars, collide. The detectors that observe these signals now record hundreds of such events per year, and the rate is expected to grow to several events per day within the next few years. Sifting through this flood of data to find the rare astrophysical signals buried in detector noise, and then alerting telescopes around the world quickly enough to capture the light produced by a collision, is one of the most demanding computational problems in modern science. This project develops an open, community machine learning framework that makes gravitational-wave discovery faster, more accurate, and more accessible. The framework will reduce the time between the crossing of a gravitational wave through the detectors and a public astronomical alert to less than a second, make it possible to detect events that traditional analysis methods miss, and lower the computing cost of these analyses by orders of magnitude. The project trains the next generation of scientists, including high-school students, undergraduates, graduate students, and researchers at smaller institutions, by sharing open code, open data, open trained models, and open lessons. It also strengthens shared national computing infrastructure that benefits not just gravitational-wave science but also particle physics, neutrino astronomy, and time-domain astronomy more broadly, advancing the national interest by accelerating discovery and broadening participation in science.\r\n\r\nThe project develops ml4gw, an open-source PyTorch-based machine learning (ML) framework for gravitational wave (GW) data analysis. ml4gw provides Graphics Processing Unit (GPU)-accelerated implementations of the data ingestion, signal-processing, waveform-generation, and inference operations that historically ran on central-processing-unit clusters of the Laser Interferometer Gravitational-wave Observatory (LIGO) Data Grid, and integrates the resulting models into the international low-latency alert pipeline. The award covers three coordinated work packages. The first extends model coverage to long-duration binary-neutron-star and neutron-star-black-hole signals using multi-rate and multi-band processing, integrates auxiliary detector channels through multimodal architectures, and develops state-space and ensemble models together with a shared foundation backbone from which task-specific models can be fine-tuned. The second work package builds production-grade cyberinfrastructure that targets the National Artificial Intelligence Research Resource, the National Research Platform, and the Open Science Data Federation, including an Inference-as-a-Service deployment built on the NVIDIA Triton inference server and on the SuperSONIC service that already supports particle and neutrino physics experiments. The third work package delivers community resources: standardized benchmark datasets with persistent digital object identifiers on Zenodo, versioned reference models on Hugging Face, comprehensive documentation and tutorials hosted on Read the Docs, containerized release artifacts, an upgraded continuous integration system that reduces test runtime by an order of magnitude, and an agent-driven development scaffold for community-led code contribution. Training and outreach activities include hands-on tutorials at international collaboration meetings, an annual hands-on lesson at the University of Minnesota Time-Domain Astrophysics Summer School, and a public machine learning challenge focused on binary-neutron-star detection that builds on a prior challenge that engaged hundreds of teams and roughly a thousand individual participants.\r\n\r\nThis award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program within the Physics Section of the Directorate for Mathematical and Physical Sciences.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"REGENTS OF THE UNIVERSITY OF MINNESOTA","awardeeAddress":"2221 UNIVERSITY AVE SE STE 100","awardeeCity":"MINNEAPOLIS","awardeeCountryCode":"US","awardeeDistrict":"05","awardeeDistrictCode":"MN05","awardeeName":"University of Minnesota-Twin Cities","awardeePhone":"6126245599","awardeeStateCode":"MN","awardeeZipCode":"554143074","cfdaNumber":"47.049, 47.070","coPDPI":["Michael W Coughlin cough052@umn.edu"],"date":"06/01/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"880650","expDate":"09/30/2029","fundAgencyCode":"4900","fundProgramName":"Software Institutes, PHYSICS AT THE INFO FRONTIER, NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $880,650.00"],"fundsObligatedAmt":"880650","histAwd":"false","id":"2609004","initAmendmentDate":"06/01/2026","latestAmendmentDate":"06/01/2026","managingPec":"800400","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Ali Anwar","perfAddress":"2221 UNIVERSITY AVE SE STE 100","perfCity":"MINNEAPOLIS","perfCountryCode":"US","perfDistrict":"05","perfDistrictCode":"MN05","perfLocation":"University of Minnesota-Twin Cities","perfStateCode":"MN","perfZipCode":"554143074","pi":["Ali Anwar aanwar@umn.edu"],"piEmail":"aanwar@umn.edu","piFirstName":"Ali","piId":"270076402","piLastName":"Anwar","poEmail":"mpierce@nsf.gov","poName":"Marlon Pierce","poPhone":"7032927743","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"800400, 755300, 296Y00","program":"Artificial Intelligence (AI), Software Institutes","progRefCode":"075Z, 8004","publicAccessMandate":"1","startDate":"10/01/2026","title":"Collaborative Research: Frameworks: ML4GW, a machine learning ecosystem for gravitational wave data analysis","transType":"Standard Grant","ueiNumber":"KABJZBBJ4B54"},{"abstractText":"Since the first direct observation of gravitational waves in 2015, a new field of astronomy has fundamentally changed how the universe can be explored. A gravitational wave is a ripple in spacetime produced when two extremely dense objects, such as black holes or neutron stars, collide. The detectors that observe these signals now record hundreds of such events per year, and the rate is expected to grow to several events per day within the next few years. Sifting through this flood of data to find the rare astrophysical signals buried in detector noise, and then alerting telescopes around the world quickly enough to capture the light produced by a collision, is one of the most demanding computational problems in modern science. This project develops an open, community machine learning framework that makes gravitational-wave discovery faster, more accurate, and more accessible. The framework will reduce the time between the crossing of a gravitational wave through the detectors and a public astronomical alert to less than a second, make it possible to detect events that traditional analysis methods miss, and lower the computing cost of these analyses by orders of magnitude. The project trains the next generation of scientists, including high-school students, undergraduates, graduate students, and researchers at smaller institutions, by sharing open code, open data, open trained models, and open lessons. It also strengthens shared national computing infrastructure that benefits not just gravitational-wave science but also particle physics, neutrino astronomy, and time-domain astronomy more broadly, advancing the national interest by accelerating discovery and broadening participation in science.\r\n\r\nThe project develops ml4gw, an open-source PyTorch-based machine learning (ML) framework for gravitational wave (GW) data analysis. ml4gw provides Graphics Processing Unit (GPU)-accelerated implementations of the data ingestion, signal-processing, waveform-generation, and inference operations that historically ran on central-processing-unit clusters of the Laser Interferometer Gravitational-wave Observatory (LIGO) Data Grid, and integrates the resulting models into the international low-latency alert pipeline. The award covers three coordinated work packages. The first extends model coverage to long-duration binary-neutron-star and neutron-star-black-hole signals using multi-rate and multi-band processing, integrates auxiliary detector channels through multimodal architectures, and develops state-space and ensemble models together with a shared foundation backbone from which task-specific models can be fine-tuned. The second work package builds production-grade cyberinfrastructure that targets the National Artificial Intelligence Research Resource, the National Research Platform, and the Open Science Data Federation, including an Inference-as-a-Service deployment built on the NVIDIA Triton inference server and on the SuperSONIC service that already supports particle and neutrino physics experiments. The third work package delivers community resources: standardized benchmark datasets with persistent digital object identifiers on Zenodo, versioned reference models on Hugging Face, comprehensive documentation and tutorials hosted on Read the Docs, containerized release artifacts, an upgraded continuous integration system that reduces test runtime by an order of magnitude, and an agent-driven development scaffold for community-led code contribution. Training and outreach activities include hands-on tutorials at international collaboration meetings, an annual hands-on lesson at the University of Minnesota Time-Domain Astrophysics Summer School, and a public machine learning challenge focused on binary-neutron-star detection that builds on a prior challenge that engaged hundreds of teams and roughly a thousand individual participants.\r\n\r\nThis award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program within the Physics Section of the Directorate for Mathematical and Physical Sciences.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"MASSACHUSETTS INSTITUTE OF TECHNOLOGY","awardeeAddress":"77 MASSACHUSETTS AVE","awardeeCity":"CAMBRIDGE","awardeeCountryCode":"US","awardeeDistrict":"07","awardeeDistrictCode":"MA07","awardeeName":"Massachusetts Institute of Technology","awardeePhone":"6172531000","awardeeStateCode":"MA","awardeeZipCode":"021394301","cfdaNumber":"47.049, 47.070","coPDPI":["Philip C Harris pcharris@mit.edu"],"date":"06/01/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"973138","expDate":"09/30/2029","fundAgencyCode":"4900","fundProgramName":"Software Institutes, NAIRR-Nat AI Research Resource, PHYSICS AT THE INFO FRONTIER","fundsObligated":["FY 2026 = $973,138.00"],"fundsObligatedAmt":"973138","histAwd":"false","id":"2609003","initAmendmentDate":"06/01/2026","latestAmendmentDate":"06/01/2026","managingPec":"800400","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Erotokritos Katsavounidis","perfAddress":"77 MASSACHUSETTS AVE","perfCity":"CAMBRIDGE","perfCountryCode":"US","perfDistrict":"07","perfDistrictCode":"MA07","perfLocation":"Massachusetts Institute of Technology","perfStateCode":"MA","perfZipCode":"021394301","pi":["Erotokritos Katsavounidis kats@mit.edu"],"piEmail":"kats@mit.edu","piFirstName":"Erotokritos","piId":"269713578","piLastName":"Katsavounidis","poEmail":"mpierce@nsf.gov","poName":"Marlon Pierce","poPhone":"7032927743","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"800400, 296Y00, 755300","program":"Artificial Intelligence (AI), Software Institutes","progRefCode":"075Z, 8004","publicAccessMandate":"1","startDate":"10/01/2026","title":"Collaborative Research: Frameworks: ML4GW, a machine learning ecosystem for gravitational wave data analysis","transType":"Standard Grant","ueiNumber":"E2NYLCDML6V1"},{"abstractText":"The rapid proliferation of artificial intelligence (AI) applications has led to the deployment of large-scale, high-density, energy-intensive accelerators such as GPUs and TPUs in AI data centers. However, the expanding scale and powerful accelerators have introduced unprecedented power demands, pushing the power distribution and cooling systems to approach peak capacities. It further tightens the coupling between computation and electricity infrastructures, posing new operational risks at the intersection of AI data centers and power grids. All these factors pose serious reliability and safety threats to critical infrastructures, including AI data centers and power grids.\r\n\r\nThis project investigates power-related vulnerabilities in AI data centers and their interactions with power grids, with a particular focus on how adversarial, accidental, or cascading failures can compromise reliability, availability, and security. This project aims to systematically characterize these vulnerabilities and develop defensive mechanisms that enhance the resilience of both AI computing infrastructure and the underlying power grid. The main contributions of this project lie in the comprehensive power risk assessment, novel and effective defense design, prototype development, and thorough experimental evaluation in real scenarios. The team will synthesize the results and observations of this project into actionable intelligence, which can be leveraged for further improving the resilience and availability of AI data center and power grid infrastructures in the future.  The power safety coverage provided by this project will be instrumental in defending data center and power grid infrastructures against future malicious attacks.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"VIRGINIA POLYTECHNIC INSTITUTE & STATE UNIVERSITY","awardeeAddress":"300 TURNER ST NW","awardeeCity":"BLACKSBURG","awardeeCountryCode":"US","awardeeDistrict":"09","awardeeDistrictCode":"VA09","awardeeName":"Virginia Polytechnic Institute and State University","awardeePhone":"5402315281","awardeeStateCode":"VA","awardeeZipCode":"240603359","cfdaNumber":"47.070","coPDPI":["Xiaorui Wang xwang@ece.osu.edu","Xing Gao xgao@udel.edu"],"date":"07/20/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"1200000","expDate":"09/30/2029","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $1,200,000.00"],"fundsObligatedAmt":"1200000","histAwd":"false","id":"2613408","initAmendmentDate":"07/20/2026","latestAmendmentDate":"07/20/2026","managingPec":"802700","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Haining Wang","perfAddress":"300 TURNER ST NW","perfCity":"BLACKSBURG","perfCountryCode":"US","perfDistrict":"09","perfDistrictCode":"VA09","perfLocation":"Virginia Polytechnic Institute and State University","perfStateCode":"VA","perfZipCode":"240603359","pi":["Haining Wang hnw@vt.edu"],"piEmail":"hnw@vt.edu","piFirstName":"Haining","piId":"269749798","piLastName":"Wang","poEmail":"dmassey@nsf.gov","poName":"Daniel F. Massey","poPhone":"7032925147","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), CYBERINFRASTRUCTURE, POWER, CONTROLS & ADAPTIVE NET","progRefCode":"075Z, 7231, 7607","publicAccessMandate":"1","startDate":"10/01/2026","title":"CICI:TCR: Towards Resilient AI Data Center Infrastructure with Power Safety","transType":"Standard Grant","ueiNumber":"QDE5UHE5XD16"},{"abstractText":"Category II: PARAPET: Prototype Architecture for Research Advances using Privacy Enhancing Technologies\r\n\r\nSan Diego State University, in partnership with the University of Utah, Arizona State University, and technology partners, is building PARAPET (Prototype Architecture for Research Advances using Privacy Enhancing Technologies), an advanced community cyberinfrastructure prototype centered around Privacy Enhancing Technologies (PETs). PARAPET is designed to unlock the potential for collaboration and exploration using sensitive data within the national science and engineering research community. PARAPET contributes to the advancement of federal priorities, including future computing technologies, Artificial Intelligence (AI), science and technology workforce capacity, and building world-class research cyberinfrastructure.\r\n\r\nThe PARAPET cyberinfrastructure prototype is focused on deploying, operationalizing, and understanding the broader viability of three PETs: differential privacy, homomorphic encryption, and federated learning. A key distinguishing feature of the PETs approach is that a compromise of the cyberinfrastructure does not compromise the data. To support these primitives, PARAPET includes specialized hardware, software, and researcher support needed to explore PETs across a wide range of research domains of critical national importance, including networking, computer science, cybersecurity, life sciences, social sciences, and others. As a prototype advanced cyberinfrastructure, PARAPET will both evaluate the potential of new PETs to reduce operational expense and institutional risk as well as help inform the community’s understanding of how these technologies relate to existing compliance frameworks. Due to the distinct differences in data and privacy requirements between science projects and researchers, a team of technical privacy experts is responsible for onboarding data and tailoring technical and policy requirements, while also alleviating the burden on domain researchers to use the platform with confidence. PARAPET thus addresses the acute national need for cyberinfrastructure to support science using regulated data and will inform future cyberinfrastructures for protected data. \r\n--------\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"SAN DIEGO STATE UNIVERSITY FOUNDATION","awardeeAddress":"5250 CAMPANILE DR","awardeeCity":"SAN DIEGO","awardeeCountryCode":"US","awardeeDistrict":"51","awardeeDistrictCode":"CA51","awardeeName":"San Diego State University Foundation","awardeePhone":"6195945731","awardeeStateCode":"CA","awardeeZipCode":"92182","cfdaNumber":"47.070","coPDPI":["Manish Parashar manish.parashar@utah.edu","Carolyn A Ellis carolynEllis@asu.edu","Joann Q Chen jchen27@sdsu.edu","Michael Farley mfarley@sdsu.edu"],"date":"07/30/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"4999963","expDate":"08/31/2031","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $4,999,963.00"],"fundsObligatedAmt":"4999963","histAwd":"false","id":"2537035","initAmendmentDate":"07/30/2026","latestAmendmentDate":"11/06/2026","managingPec":"723100","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"H59JKGFZKHL7","pdPIName":"Robert Beverly","perfAddress":"5500 CAMPANILE DR","perfCity":"SAN DIEGO","perfCountryCode":"US","perfDistrict":"51","perfDistrictCode":"CA51","perfLocation":"San Diego State University","perfStateCode":"CA","perfZipCode":"921820001","pi":["Robert Beverly rbeverly@sdsu.edu"],"piEmail":"rbeverly@sdsu.edu","piFirstName":"Robert","piId":"269855479","piLastName":"Beverly","poEmail":"rchadduc@nsf.gov","poName":"Robert Chadduck","poPhone":"7032922247","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), EQUIPMENT ACQUISITIONS","progRefCode":"075Z, 7619","publicAccessMandate":"1","startDate":"09/15/2026","title":"Category II: PARAPET: Prototype Architecture for Research Advances using Privacy Enhancing Technologies","transType":"Cooperative Agreement","ueiNumber":"H59JKGFZKHL7"},{"abstractText":"Sagebrush is a transformative generative artificial intelligence (AI) inference platform, designed to accelerate scientific discovery by providing researchers with a powerful tool for AI-driven discovery. As fields such as genomics, health care, and civil engineering require the most advanced real-time analysis, traditional GPU-based systems struggle to meet the demands for speed, scalability, and sustainability. Sagebrush addresses these challenges by leveraging cutting-edge SambaNova AI processors, which deliver unmatched performance while minimizing energy consumption.\r\n \r\nHosted at the Texas Advanced Computing Center (TACC) - a leader in high-performance computing and home to the NSF’s Leadership Class Computing Facility (LCCF) - Sagebrush integrates seamlessly with existing NSF resources to ensure broad access for researchers across disciplines. The platform is optimized for AI inference workloads, enabling scientists to interact with large-scale AI models in real time.  By offering user-friendly tools like Jupyter notebooks and web-based chatbot interfaces, the platform lowers technical barriers, empowering researchers of all backgrounds to harness AI for transformative discoveries.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF TEXAS AT AUSTIN","awardeeAddress":"110 INNER CAMPUS DR","awardeeCity":"AUSTIN","awardeeCountryCode":"US","awardeeDistrict":"25","awardeeDistrictCode":"TX25","awardeeName":"University of Texas at Austin","awardeePhone":"5124716424","awardeeStateCode":"TX","awardeeZipCode":"787121139","cfdaNumber":"47.070","coPDPI":["Maytal Dahan maytal@tacc.utexas.edu","Joseph Stubbs jstubbs@tacc.utexas.edu"],"date":"07/20/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"4999956","expDate":"08/31/2028","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $4,999,956.00"],"fundsObligatedAmt":"4999956","histAwd":"false","id":"2537075","initAmendmentDate":"07/20/2026","latestAmendmentDate":"07/20/2026","managingPec":"296Y00","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"X5NKD2NFF2V3","pdPIName":"Niall I Gaffney","perfAddress":"110 INNER CAMPUS DR","perfCity":"AUSTIN","perfCountryCode":"US","perfDistrict":"25","perfDistrictCode":"TX25","perfLocation":"University of Texas at Austin","perfStateCode":"TX","perfZipCode":"787121139","pi":["Niall I Gaffney ngaffney@eid.utexas.edu"],"piEmail":"ngaffney@eid.utexas.edu","piFirstName":"Niall","piId":"269938593","piLastName":"Gaffney","piMiddeInitial":"I","poEmail":"rchadduc@nsf.gov","poName":"Robert Chadduck","poPhone":"7032922247","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), EQUIPMENT ACQUISITIONS","progRefCode":"075Z, 7619","publicAccessMandate":"1","startDate":"09/15/2026","title":"Category II: Sagebrush - An AI Inference Resource for ACCESS, NAIRR, and the Nation","transType":"Cooperative Agreement","ueiNumber":"V6AFQPN18437"},{"abstractText":"This project builds an artificial intelligence (AI) infrastructure that enhances access to computational power, datasets, and models that support research on social policy and poverty. It will develop a novel trustworthy AI infrastructure that will 1) integrate, curate, and process diverse policy-related data sets; 2) develop and facilitate access to novel AI models that can learn from existing policies, providing insights such as what makes a program successful; and 3) open new areas of research and scholarship, creating multi-disciplinary communities of policymakers, economists, computer scientists, and government and non-government organizations to better address these societal challenges.\r\n\r\nThis project demonstrates how the resources made available through the National Artificial Intelligence Research Resource (NAIRR) Pilot program can be employed to address a major social challenge - poverty.  The effort develops a novel, trustworthy AI infrastructure that combines the power of foundation models (FM) with the reasoning capabilities of probabilistic graphical models (PGM) for evidence-based poverty eradication research. This project mitigates several technical challenges related to data, models, and their evaluation, providing foundational and use-inspired advances by: 1) Developing novel methods to build a knowledge database from diverse unstructured poverty eradication literature, such as academic literature and impact evaluations; 2) Combining the linguistic capabilities of LLMs with the reasoning capabilities of a PGM to answer policy-relevant questions; and 3) Developing methods to identify, quantify, and mitigate biases that may be induced in the system, to ensure that these systems are safe and trustworthy when deployed.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK","awardeeAddress":"520 LEE ENTRANCE STE 211","awardeeCity":"AMHERST","awardeeCountryCode":"US","awardeeDistrict":"26","awardeeDistrictCode":"NY26","awardeeName":"SUNY at Buffalo","awardeePhone":"7166452634","awardeeStateCode":"NY","awardeeZipCode":"142282577","cfdaNumber":"47.070","date":"09/01/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"158000","expDate":"02/28/2027","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2025 = $124,710.00"],"fundsObligatedAmt":"124710","histAwd":"false","id":"2640830","initAmendmentDate":"09/01/2026","latestAmendmentDate":"09/01/2026","managingPec":"296Y00","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"GMZUKXFDJMA9","pdPIName":"Neeti Pokhriyal","perfAddress":"520 LEE ENTRANCE STE 211","perfCity":"AMHERST","perfCountryCode":"US","perfDistrict":"26","perfDistrictCode":"NY26","perfLocation":"SUNY at Buffalo","perfStateCode":"NY","perfZipCode":"142282577","pi":["Neeti Pokhriyal neetipok@buffalo.edu"],"piEmail":"neetipok@buffalo.edu","piFirstName":"Neeti","piId":"270102951","piLastName":"Pokhriyal","poEmail":"wtung@nsf.gov","poName":"Wen-Wen Tung","poPhone":"7032928386","primaryProgram":["01002526DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), EAGER","progRefCode":"075Z, 7916","publicAccessMandate":"1","startDate":"09/01/2026","title":"Collaborative Research: EAGER: NAIRR Pilot: An AI infrastructure to advance research in reducing poverty and informing social policy","transType":"Standard Grant","ueiNumber":"LMCJKRFW5R81"},{"abstractText":"This Faculty Early Career Development project, BRACE-CI, creates new ways for engineers and artificial intelligence (AI) to work together to transform the design and assessment of the next generation of civil infrastructure. Communities face escalating challenges from natural hazards, aging infrastructure, increasing system complexity, and environmental impacts, yet fragmented engineering workflows and disconnected information across researchers and stakeholders limit the ability to evaluate and implement safer, more efficient, and more resilient solutions. BRACE-CI addresses this challenge by developing an open cyberinfrastructure that brings human judgment and AI into a shared engineering environment. The project helps engineers assess structures more rapidly and comprehensively, explore integrated design options from concept through detailed design, and make better decisions about safety, resilience, cost, and resource efficiency. BRACE-CI prepares students and professionals to use human-aligned engineering AI responsibly in safety-critical settings. Through open educational and research resources and outreach activities, the project broadens access to advanced engineering tools and contributes to building resilient communities and the STEM workforce.\r\n\r\nBRACE-CI advances human-aligned co-engineering through master builder agents: AI systems that assist engineers across inspection, design, analysis, and coordination tasks while preserving human oversight and improving civil infrastructure outcomes. The research is organized around three connected thrusts. Co-INSPECT creates realistic virtual worlds for civil infrastructure and uses AI assistance to support rapid, explainable, and more complete assessments. Co-DESIGN creates a multidisciplinary design environment that helps architects, engineers, and builders examine coordinated building options across conceptual, preliminary, and detailed design stages. Co-TEACH brings the research platform into classrooms, mentored research experiences, and workshops, while using feedback from students and practitioners to improve the platform and guide future research. Across these thrusts, BRACE-CI integrates engineering simulations, domain knowledge, multimodal AI agents, and scalable computing resources. The resulting reproducible cyberinfrastructure supports both recursive self-improvement of agents and operation by humans in the loop. The resulting methods, educational resources, and research platforms advance interpretability and safety in AI for safety-critical engineering, accelerate resilient infrastructure design and assessment, and prepare the next generation of scientists and engineers to lead responsible human-AI collaboration.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF HOUSTON SYSTEM","awardeeAddress":"4300 MARTIN LUTHER KING BLVD","awardeeCity":"HOUSTON","awardeeCountryCode":"US","awardeeDistrict":"18","awardeeDistrictCode":"TX18","awardeeName":"University of Houston","awardeePhone":"7137435773","awardeeStateCode":"TX","awardeeZipCode":"77204","cfdaNumber":"47.070","date":"07/27/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"499999","expDate":"08/31/2031","fundAgencyCode":"4900","fundProgramName":"CAREER: FACULTY EARLY CAR DEV, NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $400,000.00"],"fundsObligatedAmt":"400000","histAwd":"false","id":"2544659","initAmendmentDate":"07/27/2026","latestAmendmentDate":"09/11/2026","managingPec":"741400","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Vedhus Hoskere","perfAddress":"4300 MARTIN LUTHER KING BLVD","perfCity":"HOUSTON","perfCountryCode":"US","perfDistrict":"18","perfDistrictCode":"TX18","perfLocation":"University of Houston","perfStateCode":"TX","perfZipCode":"772043067","pi":["Vedhus Hoskere vhoskere@central.uh.edu"],"piEmail":"vhoskere@central.uh.edu","piFirstName":"Vedhus","piId":"270048948","piLastName":"Hoskere","poEmail":"wtung@nsf.gov","poName":"Wen-Wen Tung","poPhone":"7032928386","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT","01002930DB NSF RESEARCH & RELATED ACTIVIT","01003031DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"104500, 296Y00","program":"Artificial Intelligence (AI), CAREER-Faculty Erly Career Dev, Advanced Manufacturing","progRefCode":"075Z, 1045, 8037","publicAccessMandate":"1","startDate":"09/01/2026","title":"CAREER: BRACE-CI: Building Resilience through human-AI Co-Engineering Cyberinfrastructure","transType":"Continuing Grant","ueiNumber":"QKWEF8XLMTT3"},{"abstractText":"Building on the success of the 2025 inaugural and 2026 National Artificial Intelligence Research Resource (NAIRR) Annual Meetings, Internet2 will convene the third NAIRR Annual Meeting in spring 2027. The NAIRR Pilot was launched in January 2024 as a collaborative effort led by the National Science Foundation with many federal agencies and non-governmental partners. The goals of the NAIRR Pilot are to explore and demonstrate approaches and solutions in preparation for an eventual full-scale NAIRR, with emphasis on making computational, data, models, educational resources, and collaborative opportunities broadly available to researchers and educators to accelerate research and innovation on AI and using AI. As a national, member-driven organization that operates technology and regularly convenes stakeholders in support of research and education, Internet2 will again host the 2027 NAIRR Annual Meeting.\r\n\r\nThe 2027 NAIRR Annual Meeting will bring together participating NAIRR researchers and educators, resource providers, students, and working groups to connect, exchange ideas, and share lessons learned about NAIRR capabilities, plans, and outcomes. This event will highlight research and education advancements, resources, and innovations from NAIRR contributors and users, along with opportunities to explore NAIRR resources and tools, impacts, challenges, and future possibilities. The meeting will also provide a venue for discussions on lessons learned, opportunities to enhance innovation in AI in support of research and education, the democratization of AI resources and tools, and ways of reaching new and emerging AI research and education communities. Ultimately, the 2027 Annual Meeting aims to strengthen the NAIRR community and inform strategic next steps for NAIRR and the research and education community as the program transitions from pilot to a full-scale, sustained national resource.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY CORPORATION FOR ADVANCED INTERNET DEVELOPMENT","awardeeAddress":"1150 18TH ST NW STE 750","awardeeCity":"WASHINGTON","awardeeCountryCode":"US","awardeeDistrict":"00","awardeeDistrictCode":"DC00","awardeeName":"INTERNET2","awardeePhone":"7349134264","awardeeStateCode":"DC","awardeeZipCode":"200363880","cfdaNumber":"47.070","coPDPI":["Timothy Middelkoop tmiddelkoop@internet2.edu","Lauren A Michael lmichael@internet2.edu","Stephen L Deems sdeems@internet2.edu"],"date":"07/22/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"999998","expDate":"08/31/2027","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $999,998.00"],"fundsObligatedAmt":"999998","histAwd":"false","id":"2629389","initAmendmentDate":"07/22/2026","latestAmendmentDate":"07/22/2026","managingPec":"296Y00","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Dana Brunson","perfAddress":"1150 18TH ST NW STE 750","perfCity":"WASHINGTON","perfCountryCode":"US","perfDistrict":"00","perfDistrictCode":"DC00","perfLocation":"INTERNET2","perfStateCode":"DC","perfZipCode":"200363880","pi":["Dana Brunson dbrunson@internet2.edu"],"piEmail":"dbrunson@internet2.edu","piFirstName":"Dana","piId":"269850743","piLastName":"Brunson","poEmail":"shabagch@nsf.gov","poName":"Sharmistha Bagchi-Sen","poPhone":"7032928104","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), CONFERENCE AND WORKSHOPS","progRefCode":"075Z, 7556","publicAccessMandate":"1","startDate":"09/01/2026","title":"Conference: Bringing Together the NAIRR Community for the 2027 Annual Meeting","transType":"Standard Grant","ueiNumber":"DZEBKFRD1ZN6"},{"abstractText":"The people who document, archive, and tell the stories of current and past cultural practices play an important part in societies, including how people view the past and possible futures. The growing omnipresence of generative artificial intelligence (AI) systems in society holds important implications for archival practices going into the future, making it imperative for those who do this essential cultural and archival work to understand and adequately use those same AI systems. This work connects AI and computing scholars with scholars who work in the study of digital archives and culture for mutual training and co-creation of new AI-based digital systems. As a part of this work, scholars learn and use resources provided by the National Artificial Intelligence Research Resource (NAIRR) during workshops to re-think how AI systems can and should be created to interact with specific cultural and archival practices. This research involves the exploration of current dominant uses of AI (especially generative AI) systems for certain digital archives and the development of new designs and early prototypes of possible systems based on the needs of those working on those digital archives. The research is complemented with the important educational aspect of training experts in archives and cultural practices on generative AI systems and the use of generative AI systems with NAIRR resources.\r\n\r\nThis project connects researchers from information science and computer science and engineering with a community of archival and cultural practice researchers. As a part of this project, researchers work together to create contextualized and relevant AI systems using resources provided by NAIRR, with a particular emphasis placed on using a new multi-level framework for ethical engineering of AI systems. This work involves understanding impacts of behavior across individual, organizational, and social levels as AI systems are engineered. The deeper understanding of AI systems allows more nuanced considerations of culturally contextualized digital archives so that current archives and ways of knowing amongst relevant communities can be extended and augmented, as well as to understand what the development and integration of generative AI systems mean for the future of those digital archives. This use of contextualized digital archives also gives a unique opportunity to probe existing social knowledge structures present within generative AI systems. This research integrates several disciplinary perspectives, especially those from AI, computational cognitive science, and archival studies to study potential interactions between generative AI systems and certain digital archives across levels of behavior and analysis.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"THE PENNSYLVANIA STATE UNIVERSITY","awardeeAddress":"201 OLD MAIN","awardeeCity":"UNIVERSITY PARK","awardeeCountryCode":"US","awardeeDistrict":"15","awardeeDistrictCode":"PA15","awardeeName":"Pennsylvania State Univ University Park","awardeePhone":"8148651372","awardeeStateCode":"PA","awardeeZipCode":"16802","cfdaNumber":"47.070","date":"07/21/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"299770","expDate":"08/31/2028","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $299,770.00"],"fundsObligatedAmt":"299770","histAwd":"false","id":"2546279","initAmendmentDate":"07/21/2026","latestAmendmentDate":"07/21/2026","managingPec":"296Y00","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Christopher L Dancy","perfAddress":"201 OLD MAIN","perfCity":"UNIVERSITY PARK","perfCountryCode":"US","perfDistrict":"15","perfDistrictCode":"PA15","perfLocation":"Pennsylvania State Univ University Park","perfStateCode":"PA","perfZipCode":"168021503","pi":["Christopher L Dancy cld5070@psu.edu"],"piEmail":"cld5070@psu.edu","piFirstName":"Christopher","piId":"269969789","piLastName":"Dancy","piMiddeInitial":"L","poEmail":"plsmith@nsf.gov","poName":"Plato Smith","poPhone":"7032924278","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), EAGER, Software Institutes","progRefCode":"075Z, 7916, 8004","publicAccessMandate":"1","startDate":"09/01/2026","title":"EAGER: Investigating and Training Digital Archive Communities in the Uses of Generative AI and the National AI Research Resources","transType":"Standard Grant","ueiNumber":"NPM2J7MSCF61"},{"abstractText":"Scientific research today generates data at a scale and pace that far exceeds what most research institutions can manage on their own. Petabytes of measurements from telescopes, particle accelerators, weather sensors, and medical imaging systems sit in disconnected storage systems across the country, out of reach of many scientists who could use them to drive scientific discoveries and AI innovation. Universities and colleges with limited computing resources are seldom able to participate in large-scale, data-driven research, limiting who contributes to scientific progress, workforce training, and AI readiness. This project operationalizes the National Science Data Fabric (NSDF), a national data infrastructure service funded through the NSF Integrated Data and Systems Sciences (IDSS) program, that connects researchers at institutions of all sizes to scientific data wherever it resides at national laboratories, experimental facilities, cloud platforms, leadership-class computing centers, campus clusters, or laboratory instruments while reducing the need for costly and time-consuming data movement. By removing infrastructure gaps that limit participation in national-scale research, NSDF advances the NSF's mission to promote the progress of science and advance national health, prosperity, and welfare. The project also builds the next generation of data and AI-capable scientists through a national Fellows Program, summer training institutes, and hands-on workshops open to students and early-career researchers at universities and colleges across the country.\r\n\r\nThis Category II IDSS project transitions the successful NSDF pilot (NSF Award #2138811) to a full production-grade national cyberinfrastructure and service. The core technical approach replaces the costly data-to-compute model with a federated architecture that connects computation directly to data across varied environments through standardized NSDF Entry Points deployed at campuses, national laboratories, computing centers, and cloud systems. Entry Points standardize identity management, data ingestion, metadata capture, and workflow integration across independent sites while preserving local data governance and access policies. Five integrated technical innovations drive the system: interoperable federation through open APIs, Findable, Accessible, Interoperable, and Reusable (FAIR)-aligned schemas, persistent identifiers, and federated identity services (CILogon, InCommon); AI-ready workflows and standardized AI benchmarking through MLCommons/MLPerf-integrated pipelines and containerized execution environments; automated data quality, provenance, and lineage tracking to support reproducible and verifiable AI outputs; a national user support system built on a structured five-phase user lifecycle model, a dedicated helpdesk, and AI-assisted documentation services; and continuous community co-design through domain liaisons, advisory boards, and an annual All-Hands Meeting. The NSDF-Catalog indexes datasets across scientific repositories using FAIR Digital Object standards and Croissant metadata schemas, facilitating the creation of data cohorts for AI training. As the federation grows, the project deepens integration with the National AI Research Resource (NAIRR) ecosystem, and advances long-term sustainability through consortial, academic, and industry partnership models. The project is led by the University of Tennessee Knoxville in partnership with the University of Utah, Purdue University, the Texas Advanced Computing Center (TACC), and MLCommons, with collaborating institutions across academia, national laboratories, and industry.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF TENNESSEE","awardeeAddress":"201 ANDY HOLT TOWER","awardeeCity":"KNOXVILLE","awardeeCountryCode":"US","awardeeDistrict":"02","awardeeDistrictCode":"TN02","awardeeName":"University of Tennessee Knoxville","awardeePhone":"8659743466","awardeeStateCode":"TN","awardeeZipCode":"379960001","cfdaNumber":"47.070","coPDPI":["Xiaohui Carol Song cxsong@purdue.edu","Valerio Pascucci pascucci@acm.org","Virginia A Trueheart vtrueheart@tacc.utexas.edu","Alexandros Karargyris alex@mlcommons.org"],"date":"05/22/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"9000000","expDate":"08/31/2029","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $9,000,000.00"],"fundsObligatedAmt":"9000000","histAwd":"false","id":"2609465","initAmendmentDate":"05/22/2026","jrnl":[{"artTitl":"Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling","auth":"Li, Zhimin and Menon, Harshitha and Jekel, Charles and Pascucci, Valerio and Lindstrom, Peter","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/IPDPS65963.2026.00059","jrnlYr":"2026","parPblcId":"10716466"},{"artTitl":"CIVS: A Collective-Intelligence Ensemble for Automated Software Vulnerability Scoring","auth":"Mirtaheri, Seyedeh Leili and Shahbazian, Reza and Pascucci, Valerio and Movahedkor, Narges and Pugliese, Andrea","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/ACCESS.2025.3622663","jrnlTitl":"IEEE Access","jrnlVol":"13","jrnlYr":"2025","parPblcId":"10716470"},{"artTitl":"Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics","auth":"Panta, Aashish and Sahistan, Alper and Huang, Xuan and Gooch, Amy A and Scorzelli, Giorgio and Torres, Hector and Klein, Patrice and Ovando-Montejo, Gustavo A and Lindstrom, Peter and Pascucci, Valerio","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/TVCG.2025.3642878","jrnlTitl":"IEEE Transactions on Visualization and Computer Graphics","jrnlVol":"32","jrnlYr":"2026","parPblcId":"10716467"},{"artTitl":"Large Data Acquisition and Analytics at Synchrotron Radiation Facilities","auth":"Panta, Aashish and Scorzelli, Giorgio and Gooch, Amy A and Sun, Werner and Shanks, Katherine S and Sarker, Suchismita and Bougie, Devin and Soloway, Keara and Verberg, Rolf and Berman, Tracy and Tarcea, Glenn and Allison, John and Taufer, Michela and Pasc","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/BigData66926.2025.11402408","jrnlYr":"2025","parPblcId":"10716469"},{"artTitl":"The Making of a Community Dark Matter Dataset with the National Science Data Fabric","auth":"Roberts, Amy and Marquez, Jack and NG, Kin Hong and Mickelson, Kitty and Panta, Aashish and Scorzelli, Giorgio and Gooch, Amy and Cushman, Prisca and Fritts, Matthew and Neog, Himangshu and Pascucci, Valerio and Taufer, Michela","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/MCSE.2026.3708720","jrnlTitl":"Computing in Science & Engineering","jrnlYr":"2026","parPblcId":"10710201"},{"artTitl":"Materializing Inter-Channel Relationships With Multi-Density Woodcock Tracking","auth":"Sahistan, Alper and Zellmann, Stefan and Miao, Haichao and Morrical, Nate and Wald, Ingo and Pascucci, Valerio","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/TVCG.2026.3653310","jrnlTitl":"IEEE Transactions on Visualization and Computer Graphics","jrnlVol":"32","jrnlYr":"2026","parPblcId":"10716468"},{"artTitl":"Lowering Barriers and Raising Engagement: Stewardship Lessons From Faculty at Primarily Undergraduate Institutions Using National Cyberinfrastructure","auth":"Sutherlin, Michael and Kirkpatrick, Christine R and Pascucci, Valerio and Taufer, Michela","authIndCode":"N","dgtlObjId":"https://doi.org/10.1109/MCSE.2026.3665291","jrnlTitl":"Computing in Science & Engineering","jrnlVol":"28","jrnlYr":"2026","parPblcId":"10681740"}],"latestAmendmentDate":"05/22/2026","managingPec":"772600","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"RZ1YV5AUBN39","pdPIName":"Michela Taufer","perfAddress":"201 ANDY HOLT TOWER","perfCity":"KNOXVILLE","perfCountryCode":"US","perfDistrict":"02","perfDistrictCode":"TN02","perfLocation":"University of Tennessee Knoxville","perfStateCode":"TN","perfZipCode":"379960001","pi":["Michela Taufer taufer@utk.edu"],"piEmail":"taufer@utk.edu","piFirstName":"Michela","piId":"269758164","piLastName":"Taufer","poEmail":"akanaev@nsf.gov","poName":"Andrey Kanaev","poPhone":"7032922841","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), DATANET","progRefCode":"075Z, 7726","publicAccessMandate":"1","publicationResearch":["2026~Li, Zhimin and Menon, Harshitha and Jekel, Charles and Pascucci, Valerio and Lindstrom, Peter~https://doi.org/10.1109/IPDPS65963.2026.00059~Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling~N~10716466~10716466~OSTI~2026-09-29 14:37:15.5","IEEE Access~2025~13~Mirtaheri, Seyedeh Leili and Shahbazian, Reza and Pascucci, Valerio and Movahedkor, Narges and Pugliese, Andrea~https://doi.org/10.1109/ACCESS.2025.3622663~CIVS: A Collective-Intelligence Ensemble for Automated Software Vulnerability Scoring~N~10716470~10716470~OSTI~2026-09-29 14:34:44.623","IEEE Transactions on Visualization and Computer Graphics~2026~32~Panta, Aashish and Sahistan, Alper and Huang, Xuan and Gooch, Amy A and Scorzelli, Giorgio and Torres, Hector and Klein, Patrice and Ovando-Montejo, Gustavo A and Lindstrom, Peter and Pascucci, Valerio~https://doi.org/10.1109/TVCG.2025.3642878~Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics~N~10716467~10716467~OSTI~2026-09-29 14:34:18.853","2025~Panta, Aashish and Scorzelli, Giorgio and Gooch, Amy A and Sun, Werner and Shanks, Katherine S and Sarker, Suchismita and Bougie, Devin and Soloway, Keara and Verberg, Rolf and Berman, Tracy and Tarcea, Glenn and Allison, John and Taufer, Michela and Pasc~https://doi.org/10.1109/BigData66926.2025.11402408~Large Data Acquisition and Analytics at Synchrotron Radiation Facilities~N~10716469~10716469~OSTI~2026-09-29 14:37:45.713","Computing in Science & Engineering~2026~Roberts, Amy and Marquez, Jack and NG, Kin Hong and Mickelson, Kitty and Panta, Aashish and Scorzelli, Giorgio and Gooch, Amy and Cushman, Prisca and Fritts, Matthew and Neog, Himangshu and Pascucci, Valerio and Taufer, Michela~https://doi.org/10.1109/MCSE.2026.3708720~The Making of a Community Dark Matter Dataset with the National Science Data Fabric~N~10710201~10710201~OSTI~2026-09-29 14:32:46.436","IEEE Transactions on Visualization and Computer Graphics~2026~32~Sahistan, Alper and Zellmann, Stefan and Miao, Haichao and Morrical, Nate and Wald, Ingo and Pascucci, Valerio~https://doi.org/10.1109/TVCG.2026.3653310~Materializing Inter-Channel Relationships With Multi-Density Woodcock Tracking~N~10716468~10716468~OSTI~2026-09-29 14:33:52.41","Computing in Science & Engineering~2026~28~Sutherlin, Michael and Kirkpatrick, Christine R and Pascucci, Valerio and Taufer, Michela~https://doi.org/10.1109/MCSE.2026.3665291~Lowering Barriers and Raising Engagement: Stewardship Lessons From Faculty at Primarily Undergraduate Institutions Using National Cyberinfrastructure~N~10681740~10681740~OSTI~2026-09-29 14:33:20.803"],"startDate":"09/01/2026","title":"Category II: Transitioning the National Science Data Fabric Pilot into a National Operational Cyberinfrastructure and Service for Democratized, AI-Driven Scientific Discovery","transType":"Cooperative Agreement","ueiNumber":"FN2YCS2YAUW3"},{"abstractText":"Computational enzyme engineering has the potential to accelerate the discovery of catalysts for sustainable manufacturing, medicine, biotechnology, and environmental applications. However, progress is limited by the fragmented state of enzyme data. These data are often scattered across publications, supplementary files, figures, and separate databases. They are difficult to compare, reuse, or analyze at scale, especially for researchers who lack specialized programming or database expertise. This project develops Mutexa.Data, an open and community accessible data infrastructure that transforms scattered enzyme knowledge into reliable, searchable, and usable resources. The project strengthens the data foundation for artificial intelligence assisted enzyme discovery and enables research relevant to sustainable biomanufacturing and biotechnology. The project also supports education and workforce development through web-based tools, tutorials, workshops, and integration into undergraduate and graduate courses, enabling students, instructors, and researchers to unlock the full potential of enzymology database through intuitive natural language interfaces.\r\n\r\nThe project has two aims. The first aim is a large language model-based data extractor that inspects, validates, and enriches enzyme information from scientific literature and existing databases. This extractor uses document back checking, citation grounded verification, multi-agent error detection, sequence alignment, cross database normalization, and recognition of chemical information in figures to connect enzyme data with verified sequences, reactions, structures, and experimental metadata. It expands beyond standard kinetic constants to include broader properties such as activity, fitness, yield, selectivity, thermostability, solubility, inhibition, and assay conditions in a unified schema with quantified provenance and confidence. The second component is a large language model-based data navigator that allows users to query, clean, analyze, and interpret enzyme data through natural language. This navigator translates user questions into structured database queries, performs statistical and machine learning analyses, reports data quality information, and supports hypothesis driven reasoning grounded in literature evidence. Together, these components convert static enzyme databases into an interactive cyberinfrastructure for reliable data access, reproducible analysis, and artificial intelligence ready dataset generation. The platform is distributed through open-source software, public web interfaces, application programming interfaces, and community governed development, providing a sustainable foundation for enzyme engineering research and education. \r\n\r\nThis award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate of Biological Sciences.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"VANDERBILT UNIVERSITY","awardeeAddress":"110 21ST AVE S","awardeeCity":"NASHVILLE","awardeeCountryCode":"US","awardeeDistrict":"05","awardeeDistrictCode":"TN05","awardeeName":"Vanderbilt University","awardeePhone":"6153222631","awardeeStateCode":"TN","awardeeZipCode":"372032416","cfdaNumber":"47.070, 47.074","date":"07/11/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"600000","expDate":"08/31/2029","fundAgencyCode":"4900","fundProgramName":"Capacity: Cyberinfrastructure, NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $600,000.00"],"fundsObligatedAmt":"600000","histAwd":"false","id":"2608754","initAmendmentDate":"07/11/2026","latestAmendmentDate":"07/11/2026","managingPec":"800400","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Zhongyue Yang","perfAddress":"110 21ST AVE S","perfCity":"NASHVILLE","perfCountryCode":"US","perfDistrict":"05","perfDistrictCode":"TN05","perfLocation":"Vanderbilt University","perfStateCode":"TN","perfZipCode":"372032416","pi":["Zhongyue Yang zhongyue.yang@vanderbilt.edu"],"piEmail":"zhongyue.yang@vanderbilt.edu","piFirstName":"Zhongyue","piId":"270063129","piLastName":"Yang","poEmail":"wtung@nsf.gov","poName":"Wen-Wen Tung","poPhone":"7032928386","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"168Y00, 296Y00","program":"Artificial Intelligence (AI), Software Institutes, Biotechnology","progRefCode":"075Z, 8004, 8038","publicAccessMandate":"1","startDate":"09/01/2026","title":"Elements: Building Large-Language Model-Based Data Infrastructure for Enzyme Biotechnology","transType":"Standard Grant","ueiNumber":"GTNBNWXJ12D5"},{"abstractText":"Modern biological research increasingly relies on large-scale computing, artificial intelligence, and machine learning algorithms to analyze complex data sets. However, many university biology programs lack formal coursework in advanced computational methods, placing the burden on each independent researcher to learn the computing skills necessary to conduct their research. This knowledge gap limits scientific progress and the effective utilization of national computing resources. This project addresses this fundamental problem by developing an accessible, open-source training platform specifically designed for life scientists, providing hands-on, interactive training directly on the computing systems that scientists use for their day-to-day research needs. This effort will help serve the national interest by advancing research and discovery in medicine and healthcare, environmental science, and wildlife conservation.  \r\n\r\n\r\nThe goal of this project is to develop and deploy an open-source training platform as a native interactive application within the Open OnDemand web portal framework. To ensure portability across various supercomputing systems, the team of researchers will utilize containerized execution environments tightly coupled with a split-screen instructional interface. The technical approach involves developing a frontend application that integrates directly with job scheduling software, allowing users to run computations and follow instructional materials simultaneously without switching interfaces. Research activities include developing the software architecture, creating customized software containers for each lesson, and designing a modular curriculum that ranges from basic system navigation to advanced applications like deep learning for biological imaging data and genomic sequence analysis. Additionally, the project will conduct comprehensive beta testing and host workshops to teach system facilitators how to deploy the platform at their own institutions. Ultimately, this project contributes a highly extensible, portable educational framework that lowers the barrier to entry for utilizing advanced computational resources, providing a seamless transition for users from training directly to applied research.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"TEXAS A & M UNIVERSITY","awardeeAddress":"400 HARVEY MITCHELL PKY S STE 300","awardeeCity":"COLLEGE STATION","awardeeCountryCode":"US","awardeeDistrict":"10","awardeeDistrictCode":"TX10","awardeeName":"Texas A&M University","awardeePhone":"9798626777","awardeeStateCode":"TX","awardeeZipCode":"778454375","cfdaNumber":"47.070","coPDPI":["Nicole M Foley nfoley@cvm.tamu.edu","Joshua Winchell jwinchell@tamu.edu","Charles M Dickens cmdickens@tamu.edu"],"date":"07/29/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"299999","expDate":"08/31/2028","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $299,999.00"],"fundsObligatedAmt":"299999","histAwd":"false","id":"2612544","initAmendmentDate":"07/29/2026","latestAmendmentDate":"07/29/2026","managingPec":"296Y00","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Wesley A Brashear","perfAddress":"3361 TAMU","perfCity":"COLLEGE STATION","perfCountryCode":"US","perfDistrict":"10","perfDistrictCode":"TX10","perfLocation":"Texas A&M University","perfStateCode":"TX","perfZipCode":"778430001","pi":["Wesley A Brashear wbrashear@tamu.edu"],"piEmail":"wbrashear@tamu.edu","piFirstName":"Wesley","piId":"270036457","piLastName":"Brashear","piMiddeInitial":"A","poEmail":"shabagch@nsf.gov","poName":"Sharmistha Bagchi-Sen","poPhone":"7032928104","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI), CI Training and Professionals, CYBERINFRASTRUCTURE/SCIENCE","progRefCode":"075Z, 122Z, 7569","publicAccessMandate":"1","startDate":"09/01/2026","title":"CyberTraining: Pilot: Broadening Advanced CI and AI/ML Adoption in the Biological Sciences through a Distributed Open-Source Training Platform","transType":"Standard Grant","ueiNumber":"JF6XLNB4CDJ5"},{"abstractText":"ForteFAI strengthens the reliability of artificial intelligence systems that increasingly serve as critical cyberinfrastructure for science and engineering. Foundation models are now used for scientific imaging, multimodal data analysis, natural-language interfaces, and operational monitoring, yet they often fail silently when confronted with data that differ from those encountered during training. ForteFAI addresses this challenge by providing a unified framework for detecting distributional anomalies across vision, language, and cyberinfrastructure domains. The project develops open, production-ready software that improves quality control for scientific imaging, enhances safeguards for large language model workflows, and enables anomaly detection in high-performance computing operations. By identifying unreliable inputs before they propagate through downstream analyses, ForteFAI improves the robustness, reproducibility, and trustworthiness of artificial intelligence–enabled scientific discovery.\r\n\r\nThe project advances trustworthy artificial intelligence through FORTE (Finding Outliers with Representation Typicality Estimation), a model-agnostic methodology that quantifies how closely an input aligns with the representation manifold of trusted data. FORTE estimates semantic typicality using geometric manifold statistics derived from foundation model embeddings, including precision, recall, density, and coverage metrics, enabling unsupervised detection of distributional shifts without labeled anomalies, likelihood modeling, or domain-specific retraining. The research integrates this approach into three coordinated components: ForteFAI Vision for scientific image quality assurance and out-of-distribution detection; ForteFAI Text for large language model guardrails, hallucination detection, and prompt-injection monitoring; and ForteFAI Log for parser-free anomaly detection in high-performance computing logs. The resulting software, training materials, and community engagement activities will broaden access to trustworthy artificial intelligence capabilities and strengthen the national cyberinfrastructure ecosystem.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"CASE WESTERN RESERVE UNIVERSITY","awardeeAddress":"10900 EUCLID AVE","awardeeCity":"CLEVELAND","awardeeCountryCode":"US","awardeeDistrict":"11","awardeeDistrictCode":"OH11","awardeeName":"Case Western Reserve University","awardeePhone":"2163684510","awardeeStateCode":"OH","awardeeZipCode":"44106","cfdaNumber":"47.070","date":"06/30/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"600000","expDate":"07/31/2029","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource, Software Institutes","fundsObligated":["FY 2026 = $600,000.00"],"fundsObligatedAmt":"600000","histAwd":"false","id":"2608771","initAmendmentDate":"06/30/2026","latestAmendmentDate":"06/30/2026","managingPec":"800400","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Vipin Chaudhary","perfAddress":"10900 EUCLID AVE","perfCity":"CLEVELAND","perfCountryCode":"US","perfDistrict":"11","perfDistrictCode":"OH11","perfLocation":"Case Western Reserve University","perfStateCode":"OH","perfZipCode":"44106","pi":["Vipin Chaudhary vipin@case.edu"],"piEmail":"vipin@case.edu","piFirstName":"Vipin","piId":"000217890","piLastName":"Chaudhary","poEmail":"dmassey@nsf.gov","poName":"Daniel F. Massey","poPhone":"7032925147","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00, 800400","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"08/01/2026","title":"Elements: ForteFAI - Trustworthy AI Cyberinfrastructure for Guardrailing Multimodal Foundation Models and Cyberinfrastructure","transType":"Standard Grant","ueiNumber":"HJMKEF7EJW69"},{"abstractText":"This project aims to develop a plan for a national scale operational data infrastructure for personal data, i.e., human and related data containing sensitive information. This project will engage the key stakeholders to develop a coherent strategy for the design, deployment, and operation of an innovative data services platform. The envisioned platform will be a unique resource for US researchers, educators and practitioners and will enable unprecedented access to high-quality personal data along with the analytical tools and computing capabilities to launch advanced data-oriented scientific workflows. Enabling access to such data will be transformative for researchers across a broad spectrum of scientific disciplines, driving significant advances in medicine, social sciences, and artificial intelligence. The platform will be a unique addition to the NSF funded cyberinfrastructure ecosystem and will be designed to be interoperable with other relevant data-focused investments and be available through national programs such as the National Artificial Intelligence Research Resource (NAIRR) and the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS). \r\n \r\nWhile enabling broad access to personal data has obvious and potentially transformative benefits for the research community, a data platform for such data needs to consider the substantial ethical responsibilities and significant privacy risks associated with underlying data. This project focuses on an urgently needed cyberinfrastructure solution that maximizes data utility for public benefit while rigorously protecting individual privacy and maintaining public trust. This project will gather the technical requirements for the envisioned data platform to support a broad spectrum of research disciplines; identify the common infrastructure, security and privacy requirements for the platform; define path for integration with other computing and data infrastructure platforms in the NSF cyberinfrastructure ecosystem; develop the economic model to sustain the platform as a national resource; and finally, use the community input to develop a concrete design plan for the target platform.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK","awardeeAddress":"520 LEE ENTRANCE STE 211","awardeeCity":"AMHERST","awardeeCountryCode":"US","awardeeDistrict":"26","awardeeDistrictCode":"NY26","awardeeName":"SUNY at Buffalo","awardeePhone":"7166452634","awardeeStateCode":"NY","awardeeZipCode":"142282577","cfdaNumber":"47.070","coPDPI":["Sharon J Hewner hewner@buffalo.edu","Kenneth Joseph kjoseph@buffalo.edu","Varun Chandola (Former) chandola@buffalo.edu"],"date":"07/23/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"484422","expDate":"07/31/2028","fundAgencyCode":"4900","fundProgramName":"Data Cyberinfrastructure","fundsObligated":["FY 2026 = $484,422.00"],"fundsObligatedAmt":"484422","histAwd":"false","id":"2609498","initAmendmentDate":"07/23/2026","latestAmendmentDate":"09/21/2026","managingPec":"772600","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"GMZUKXFDJMA9","pdPIName":"Varun Chandola","perfAddress":"338 Davis Hall","perfCity":"AMHERST","perfCountryCode":"US","perfDistrict":"26","perfDistrictCode":"NY26","perfLocation":"SUNY at Buffalo","perfStateCode":"NY","perfZipCode":"142602500","pi":["Varun Chandola chandola@buffalo.edu","Sharon J Hewner (Former) hewner@buffalo.edu"],"piEmail":"chandola@buffalo.edu","piFirstName":"Varun","piId":"000214075","piLastName":"Chandola","poEmail":"akanaev@nsf.gov","poName":"Andrey Kanaev","poPhone":"7032922841","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"772600","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"08/01/2026","title":"Category III: Planning for a National Scale Data Platform for Personal Data","transType":"Standard Grant","ueiNumber":"LMCJKRFW5R81"},{"abstractText":"The goal of project MESA (Multidisciplinary Environment for Scientific Advancement) is to build a shared, open-source platform in which scientific data from many fields are automatically described, organized, and connected, so any researcher can find and use them in minutes to hours instead of weeks to months. At its core, metadata-enabled scientific agents read each new dataset, attach descriptions drawn from community-curated standards, and recommend how it can be combined with related information from other disciplines. It will enable researchers spanning diverse disciplines, such as astronomy, biology, environment, public health, and computer science to expedite their discoveries and engage in seamless cross-disciplinary collaboration. The project will be developed and tested with established NSF-supported synthesis centers in environmental data science (ESIIL) and molecular and cellular science (NCEMS), and in AI in agriculture (AIIRA), as well as the international Event Horizon Telescope (EHT) Collaboration. The MESA outreach model ensures that benefits reach real use cases across the range of institutions with diverse levels of research activity and that graduate students, postdoctoral researchers, and professional staff are trained in the design and use of trustworthy, agentic AI for science.\r\n \r\nMESA will be implemented as a federated, cloud-native data-mesh and data lakehouse coupled to an agentic AI layer operating as an integrated data system and service. There are three coordinated technical objectives: agentic AI-powered metadata generation; cross-domain data integration linking datasets through interoperable APIs, shared ontologies, and policy-driven governance; and agentic orchestration managing workflows and translating user intent into reproducible research objects. MESA will operate on public NSF cyberinfrastructure accessed through ACCESS-CI (Texas Advanced Computing Center, Open Storage Network, CyVerse, and Jetstream-2 Cloud) and the integrated Rule-Oriented Data System (iRODS)  from the Renaissance Computing Institute (RENCI). The two-year prototype effort includes automated metadata generation for astronomy simulations, life-science data repositories, multi-omics integration, precision agriculture, sensor networks, and environmental data synthesis to demonstrate platform capability.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF NEW MEXICO","awardeeAddress":"1 UNIVERSITY OF NEW MEXICO","awardeeCity":"ALBUQUERQUE","awardeeCountryCode":"US","awardeeDistrict":"01","awardeeDistrictCode":"NM01","awardeeName":"University of New Mexico","awardeePhone":"5052774186","awardeeStateCode":"NM","awardeeZipCode":"871310001","cfdaNumber":"47.070","date":"07/26/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"4617408","expDate":"05/31/2029","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $4,617,408.00"],"fundsObligatedAmt":"4617408","histAwd":"false","id":"2632685","initAmendmentDate":"07/26/2026","latestAmendmentDate":"07/26/2026","managingPec":"772600","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Tyson L Swetnam","perfAddress":"1 UNIVERSITY OF NEW MEXICO","perfCity":"ALBUQUERQUE","perfCountryCode":"US","perfDistrict":"01","perfDistrictCode":"NM01","perfLocation":"University of New Mexico","perfStateCode":"NM","perfZipCode":"871310001","pi":["Tyson L Swetnam tswetnam@unm.edu"],"piEmail":"tswetnam@unm.edu","piFirstName":"Tyson","piId":"269943299","piLastName":"Swetnam","piMiddeInitial":"L","poEmail":"akanaev@nsf.gov","poName":"Andrey Kanaev","poPhone":"7032922841","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"07/15/2026","title":"Research Infrastructure: Category II: MESA (Multidisciplinary Environment for Scientific Advancement)","transType":"Cooperative Agreement","ueiNumber":"F6XLTRUQJEN4"},{"abstractText":"SAFE-AI: Sandbox for Accelerated Feedback and Experimentation for AI Innovation is a two-year effort to develop and launch a testbed supported by a AI innovation ecosystem built on novel approaches to public-private partnerships. This effort brings together state government, academia, and leading industry partners to drive R&D, foster innovation and adoption, and lay the groundwork for an AI-literate workforce. Further, it seeks to support both advancements in the broad of adoption of AI in various sectors, with the needs of regulatory compliance in sensitive sectors such as healthcare.  \r\n\r\nThe proposal will enable the exploration of novel models for AI adoption in regulated environments across three key use-cases: AI-Assisted Radiological Interpretation, Mental Health Assessment and Education, and Runtime Enforcement Infrastructure.  \r\n\r\nThis work is led by the SAFE-AI advisory committee, which includes experts from across the state of Utah, including a variety of other academic institutions, government entities, and business leaders (including Mindshare Ventures, Ascend AI Labs, and Clarion AI Partners). The committee will guide the development and deployment of the pilot testbed and its innovation sandboxes, as well as execution of use cases and models for public-private partnerships.  \r\n\r\nThe testbed will also engage with related national efforts, including the NSF National AI Research Resource (NAIRR). The project will result in the coordination and integration of infrastructure supporting the testbed, formalize and strengthen public-private partnerships, and create strategies for industry to adapt and implement various regulatory schemas in complex sectors in order to reach the market. \r\n\r\nThe testbed SAFE-AI testbed will leverage and build on AWS Innovation Sandboxes to provide secure, isolated, and auditable environments for industry leaders and researchers to experiment, demonstrate concepts, and evaluate the results. Together with AWS, the SAFE-AI innovation sandbox will customize the testbed itself to realize a controlled environment to test AI product readiness to enter and persist in a regulated sector. \r\n\r\nThe activities supported by this award have a significant chance to advance national priorities, bolster regional economic growth, and advance technological progress. Specifically, the development of quantum applications for grid security are highly relevant to U.S. national security, infrastructure resilience, and global competitiveness.   \r\n\r\nInvestment in this project is both consistent and well-aligned with the goals and broad strategic objectives of NSF TIP. This investment is consistent with agency’s goals and the scientific and technological community’s interest in accelerating the adoption of AI applications in high-impact sectors, including sectors where regulatory frameworks are already pre-existing.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF UTAH","awardeeAddress":"201 PRESIDENTS CIR","awardeeCity":"SALT LAKE CITY","awardeeCountryCode":"US","awardeeDistrict":"01","awardeeDistrictCode":"UT01","awardeeName":"University of Utah","awardeePhone":"8015816903","awardeeStateCode":"UT","awardeeZipCode":"841129049","cfdaNumber":"47.084","coPDPI":["Nina de Lacy nina.delacy@utah.edu","Penny R Atkins penny.atkins@utah.edu"],"date":"06/30/2026","dirAbbr":"TIP","divAbbr":"TCP","estimatedTotalAmt":"2164348","expDate":"06/30/2028","fundAgencyCode":"4900","fundProgramName":"TIP-CHIPS KTA-1 AI-ML","fundsObligated":["FY 2026 = $2,164,348.00"],"fundsObligatedAmt":"2164348","histAwd":"false","id":"2538883","initAmendmentDate":"06/30/2026","latestAmendmentDate":"06/30/2026","managingPec":"226Y00","orgCodeDir":"15000000","orgCodeDiv":"15200000","orgLongName":"Directorate for Technology, Innovation, and Partnerships","orgLongName2":"TIP - Core Programs","orgUrl":"https://www.nsf.gov/tip","parentUeiNumber":"","pdPIName":"Manish Parashar","perfAddress":"201 PRESIDENTS CIR","perfCity":"SALT LAKE CITY","perfCountryCode":"US","perfDistrict":"01","perfDistrictCode":"UT01","perfLocation":"University of Utah","perfStateCode":"UT","perfZipCode":"841129049","pi":["Manish Parashar manish.parashar@utah.edu"],"piEmail":"manish.parashar@utah.edu","piFirstName":"Manish","piId":"000268323","piLastName":"Parashar","poEmail":"jthongno@nsf.gov","poName":"Joda Thongnopnua","poPhone":"7032924707","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"306Y00","program":"","progRefCode":"","publicAccessMandate":"1","startDate":"07/01/2026","title":"SAFE-AI: Sandbox for Accelerated Feedback and Experimentation for AI Innovation","transType":"Standard Grant","ueiNumber":"LL8GLEVH6MG3"},{"abstractText":"National Data Platform (NDP) addresses the challenge of data and computing resource integration facilitating the nation's capacity to pursue scalable AI-driven discovery and innovation by establishing a federated and interoperable ecosystem that connects distributed data repositories and computing facilities across the U.S. NDP will reduce barriers of entry for data-driven science and enable researchers, educators, and learners to accelerate their research and innovation objectives. It will expand access to advanced capabilities and unlock the nation's full research potential across regions, institutions, sectors, and disciplines. NDP will also enable hands-on experiential learning, curriculum development, data challenges, and workforce training aligned with national priorities in AI and data science. By federating existing public investments and engaging private resources into an accessible AI innovation ecosystem, NDP will strengthen U.S. competitiveness and expand both research participation and workforce engagement in an increasingly AI-driven world.\r\n\r\nNDP will integrate geographically distributed data, computing, and AI resources and services into a cohesive, federated framework that supports end-to-end, AI-enabled scientific workflows. This will be delivered through interoperable, production-grade services and middleware that streamline secure access, integration, composability, and use of data, models, and workflows across edge, cloud, and high-performance computing (HPC) resources. By enabling heterogeneous and multimodal data types to seamlessly connect with AI-optimized computing capabilities, NDP will enable new classes of multimodal, data-intensive workflows that advance scientific efficiency, reproducibility, and methodological rigor. Its scalable distributed architecture will allow users to move from data ingestion and fusion to model training, simulation, and inference, within a unified environment, reducing the barriers associated with today’s fragmented cyberinfrastructure. NDP will employ a structured build–test–validate–deploy pipeline to mature project-developed and community-contributed tools into robust, reusable, and production-ready digital assets. Through co-design with domain scientists, applications across multiple disciplines will expose shared workflow patterns that NDP will generalize into extensible, reusable services for broad adoption. Collectively, these approaches will expand capacity across the U.S. to pursue scalable, AI-enabled scientific discovery and innovation and strengthen the nation’s leadership in AI.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF CALIFORNIA, SAN DIEGO","awardeeAddress":"9500 GILMAN DR","awardeeCity":"LA JOLLA","awardeeCountryCode":"US","awardeeDistrict":"50","awardeeDistrictCode":"CA50","awardeeName":"University of California-San Diego","awardeePhone":"8585344896","awardeeStateCode":"CA","awardeeZipCode":"920930021","cfdaNumber":"47.070","coPDPI":["Manish Parashar manish.parashar@utah.edu","Maytal Dahan maytal@tacc.utexas.edu","Sarah A Stone sstone3@uw.edu","Rafael Coelho Lopes de Sa rclsa@umass.edu"],"date":"05/22/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"24500000","expDate":"06/30/2031","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource, Data Cyberinfrastructure","fundsObligated":["FY 2026 = $11,000,000.00"],"fundsObligatedAmt":"11000000","histAwd":"false","id":"2609447","initAmendmentDate":"05/22/2026","latestAmendmentDate":"05/22/2026","managingPec":"772600","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Ilkay Altintas","perfAddress":"9500 GILMAN DR","perfCity":"LA JOLLA","perfCountryCode":"US","perfDistrict":"50","perfDistrictCode":"CA50","perfLocation":"University of California-San Diego","perfStateCode":"CA","perfZipCode":"920930505","pi":["Ilkay Altintas altintas@sdsc.edu"],"piEmail":"altintas@sdsc.edu","piFirstName":"Ilkay","piId":"269778721","piLastName":"Altintas","poEmail":"akanaev@nsf.gov","poName":"Andrey Kanaev","poPhone":"7032922841","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT","01002829DB NSF RESEARCH & RELATED ACTIVIT","01003031DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00, 772600","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"07/01/2026","title":"Category I: National Data Platform: A Federated National AI-ready Data Ecosystem for Discovery, Innovation, Education, and Workforce Development","transType":"Cooperative Agreement","ueiNumber":"UYTTZT6G9DT1"},{"abstractText":"The Interactive Discovery Laboratory (iDLab) a unified, easy-to-use web-based platform that enables researchers to seamlessly access and use computing and data resources across five NSF-supported high-performance computing (HPC) sites and two public cloud platforms. iDLab provides a consistent catalog of interactive applications, access to local data resources, and a shared data partition accessible across all sites. By mitigating technical barriers, reducing data transfer requirements, and maintaining a consistent application environment, iDLab empowers researchers, broadens participation in advanced computing, and accelerates data-driven and AI-enabled discovery across a wide range of scientific fields, including natural hazards engineering, spatial biology, neuroscience, geospatial analytics, and computational physics. Additionally, the platform enhances STEM education through browser-based instructional environments, supports workforce development via coordinated training and tutorials, and advances open science by promoting research outputs that are findable, accessible, interoperable, and reusable (FAIR), thereby maximizing the impact and reach of federal investments in research infrastructure.\r\n\r\nLed by the University of California, Los Angeles (UCLA), in collaboration with the National Center for Supercomputing Applications (NCSA), the Pittsburgh Supercomputing Center (PSC), Purdue University, the San Diego Supercomputer Center (SDSC), and the Texas Advanced Computing Center (TACC), iDLab transitions the OneSciencePlace platform from a pilot to a national-scale, data-centric production service spanning NSF-supported HPC resources and two public cloud platforms. iDLab provides a consistent interactive application catalog across all partner sites, dedicated GPU and CPU resources for interactive workloads, and a shared filesystem that enables near-real-time cross-site data access without manual data staging. The full research data lifecycle, from ingestion and interactive analysis through collaborative sharing and FAIR publication is supported by the platform. iDLab complements and extends ACCESS and the National AI Research Resource (NAIRR) by providing the interactive layer for cross-facility data access and rapid scientific exploration. Throughout operations, iDLab continues to evolve through ongoing enhancements to platform, applications, data services, and the user experience, driven by operational experience and community needs.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF CALIFORNIA, LOS ANGELES","awardeeAddress":"10889 WILSHIRE BLVD STE 700","awardeeCity":"LOS ANGELES","awardeeCountryCode":"US","awardeeDistrict":"36","awardeeDistrictCode":"CA36","awardeeName":"University of California-Los Angeles","awardeePhone":"3107940102","awardeeStateCode":"CA","awardeeZipCode":"900244200","cfdaNumber":"47.070","coPDPI":["Brett M Bode brett@illinois.edu","Amitava Majumdar majumdar@sdsc.edu","Philip D Blood blood@psc.edu","Joseph Stubbs jstubbs@tacc.utexas.edu"],"date":"05/22/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"9000000","expDate":"06/30/2029","fundAgencyCode":"4900","fundProgramName":"Data Cyberinfrastructure","fundsObligated":["FY 2026 = $9,000,000.00"],"fundsObligatedAmt":"9000000","histAwd":"false","id":"2609583","initAmendmentDate":"05/22/2026","latestAmendmentDate":"05/22/2026","managingPec":"772600","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Amit Chourasia","perfAddress":"410 Charles E Young Dr E","perfCity":"LOS ANGELES","perfCountryCode":"US","perfDistrict":"36","perfDistrictCode":"CA36","perfLocation":"University of California-Los Angeles","perfStateCode":"CA","perfZipCode":"900951557","pi":["Amit Chourasia achourasia@oarc.ucla.edu"],"piEmail":"achourasia@oarc.ucla.edu","piFirstName":"Amit","piId":"269855286","piLastName":"Chourasia","poEmail":"kthompso@nsf.gov","poName":"Kevin Thompson","poPhone":"7032924220","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"772600","program":"Artificial Intelligence (AI), CSSI-1: Cyberinfr for Sustained Scientif, Data Infrstr Bldg Blocks-DIBBs","progRefCode":"075Z, 077Z, 8048","publicAccessMandate":"1","startDate":"07/01/2026","title":"Category II: iDLab - A Federated Interactive Discovery Lab for Data-Driven Research and Education","transType":"Cooperative Agreement","ueiNumber":"RN64EPNH8JC6"},{"abstractText":"Bridges-3 is the continuation of PSC’s long standing role in providing production quality national cyberinfrastructure through the NSF Advanced Computing Systems & Services (ACSS) program. As the successor to Bridges 2, Bridges-3 carries forward the program’s core objective of delivering systems and services that adapt to rapid evolution in computing and data technologies while remaining reliable, accessible, and broadly usable by the U.S. research community. Bridges-3 offers a balanced, converged environment that integrates advanced GPU accelerated computing, high performance, large-memory CPUs, a hardened all flash parallel file system, and a high bandwidth InfiniBand fabric, providing a cohesive, scalable platform for simulation, artificial intelligence, data analytics, and complex high-performance computing workflows. \r\n\r\nThe overall architecture is informed by science drivers drawn from the critical scientific domains listed in memorandum M-25-34/NSTM-2 from the Office of Science and Technology Policy, which outlines priorities for national investment and actions in Fiscal Year 2027, including artificial intelligence, quantum information science, materials and microelectronics, biomedical research, advanced manufacturing, and energy systems. Bridges-3 will continue to integrate with science gateways, classroom instruction, and community outreach programs to ensure that advanced computing remains accessible and useful across communities. These efforts align with national goals of supporting gold-standard science, building the science and technology (S&T) workforce of the future, expanding world-class research infrastructure, and strengthening America’s S&T ecosystem. Combined with seamless interoperability with national platforms and resources such as the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) capability, the National AI Research Resource (NAIRR), and the Leadership-Class Computing Facility (LCCF), these elements ensure that Bridges-3 will provide stable and productive service to the U.S. research community while supporting the broad goals of the NSF strategic plan.\r\n--\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"CARNEGIE-MELLON UNIVERSITY","awardeeAddress":"5000 FORBES AVE","awardeeCity":"PITTSBURGH","awardeeCountryCode":"US","awardeeDistrict":"12","awardeeDistrictCode":"PA12","awardeeName":"Carnegie Mellon University","awardeePhone":"4122688746","awardeeStateCode":"PA","awardeeZipCode":"152133815","cfdaNumber":"47.070","coPDPI":["Robin Scibek flaus@psc.edu","Geoffrey R Sablosky gsablosk@andrew.cmu.edu","Mei-Yu Wang mwang7@psc.edu","Thomas J Olesky tolesky@andrew.cmu.edu"],"date":"05/31/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"10000000","expDate":"05/31/2027","fundAgencyCode":"4900","fundProgramName":"Innovative HPC","fundsObligated":["FY 2026 = $10,000,000.00"],"fundsObligatedAmt":"10000000","histAwd":"false","id":"2614022","initAmendmentDate":"05/31/2026","latestAmendmentDate":"05/31/2026","managingPec":"761900","orgCodeDir":"05000000","orgCodeDiv":"05260000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure","orgUrl":"https://www.nsf.gov/cise","parentUeiNumber":"EJ96GJK37C27","pdPIName":"Bruno Ricardi de Abreu","perfAddress":"5000 FORBES AVE","perfCity":"PITTSBURGH","perfCountryCode":"US","perfDistrict":"12","perfDistrictCode":"PA12","perfLocation":"Carnegie Mellon University","perfStateCode":"PA","perfZipCode":"152133890","pi":["Bruno Ricardi de Abreu babreu@psc.edu"],"piEmail":"babreu@psc.edu","piFirstName":"Bruno","piId":"270101407","piLastName":"Ricardi de Abreu","poEmail":"rchadduc@nsf.gov","poName":"Robert Chadduck","poPhone":"7032922247","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"761900","program":"Artificial Intelligence (AI), EQUIPMENT ACQUISITIONS, QUANTUM COMPUTING","progRefCode":"075Z, 7619, 7928","publicAccessMandate":"1","startDate":"06/01/2026","title":"Category I: Bridges-3: Integrating AI, Data & HPC to Advance American Scientific Leadership","transType":"Cooperative Agreement","ueiNumber":"U3NKNFLNQ613"},{"abstractText":"The goal of project MESA (Multidisciplinary Environment for Scientific Advancement) is to build a shared, open-source platform in which scientific data from many fields are automatically described, organized, and connected, so any researcher can find and use them in minutes to hours instead of weeks to months. At its core, metadata-enabled scientific agents read each new dataset, attach descriptions drawn from community-curated standards, and recommend how it can be combined with related information from other disciplines. It will enable researchers spanning diverse disciplines, such as astronomy, biology, environment, public health, and computer science to expedite their discoveries and engage in seamless cross-disciplinary collaboration. The project will be developed and tested with established NSF-supported synthesis centers in environmental data science (ESIIL) and molecular and cellular science (NCEMS), and in AI in agriculture (AIIRA), as well as the international Event Horizon Telescope (EHT) Collaboration. The MESA outreach model ensures that benefits reach real use cases across the range of institutions with diverse levels of research activity and that graduate students, postdoctoral researchers, and professional staff are trained in the design and use of trustworthy, agentic AI for science.\r\n \r\nMESA will be implemented as a federated, cloud-native data-mesh and data lakehouse coupled to an agentic AI layer operating as an integrated data system and service. There are three coordinated technical objectives: agentic AI-powered metadata generation; cross-domain data integration linking datasets through interoperable APIs, shared ontologies, and policy-driven governance; and agentic orchestration managing workflows and translating user intent into reproducible research objects. MESA will operate on public NSF cyberinfrastructure accessed through ACCESS-CI (Texas Advanced Computing Center, Open Storage Network, CyVerse, and Jetstream-2 Cloud) and the integrated Rule-Oriented Data System (iRODS)  from the Renaissance Computing Institute (RENCI). The two-year prototype effort includes automated metadata generation for astronomy simulations, life-science data repositories, multi-omics integration, precision agriculture, sensor networks, and environmental data synthesis to demonstrate platform capability.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"false","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF ARIZONA","awardeeAddress":"845 N PARK AVE RM 538","awardeeCity":"TUCSON","awardeeCountryCode":"US","awardeeDistrict":"07","awardeeDistrictCode":"AZ07","awardeeName":"University of Arizona","awardeePhone":"5206266000","awardeeStateCode":"AZ","awardeeZipCode":"85721","cfdaNumber":"47.070","coPDPI":["Arthur B Maccabe maccabe@arizona.edu","David S Ebert ebertd@arizona.edu","Terrell Russell tgr@renci.org","Lei Cao caolei@arizona.edu"],"date":"06/01/2026","dirAbbr":"CSE","divAbbr":"OAC","estimatedTotalAmt":"4617408","expDate":"08/31/2026","fundAgencyCode":"4900","fundProgramName":"NAIRR-Nat AI Research Resource","fundsObligated":["FY 2026 = $0.00"],"fundsObligatedAmt":"4617408","histAwd":"false","id":"2608717","initAmendmentDate":"06/01/2026","latestAmendmentDate":"06/01/2026","managingPec":"772600","orgCodeDir":"05000000","orgCodeDiv":"05090000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Office of Advanced Cyberinfrastructure (OAC)","orgUrl":"http://www.nsf.gov/cise","parentUeiNumber":"","pdPIName":"Tyson L Swetnam","perfAddress":"Bioscience Research Laboratories","perfCity":"Tucson","perfCountryCode":"US","perfDistrict":"07","perfDistrictCode":"AZ07","perfLocation":"University of Arizona","perfStateCode":"AZ","perfZipCode":"857210001","pi":["Tyson L Swetnam tswetnam@unm.edu"],"piEmail":"tswetnam@unm.edu","piFirstName":"Tyson","piId":"269943299","piLastName":"Swetnam","piMiddeInitial":"L","poEmail":"akanaev@nsf.gov","poName":"Andrey Kanaev","poPhone":"7032922841","primaryProgram":["01002627DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"296Y00","program":"Artificial Intelligence (AI)","progRefCode":"075Z","publicAccessMandate":"1","startDate":"06/01/2026","title":"Research Infrastructure: Category II: MESA (Multidisciplinary Environment for Scientific Advancement)","transType":"Cooperative Agreement","ueiNumber":"ED44Y3W6P7B9"}],"metadata":{"offset":0,"rpp":25,"totalCount":115}}}