Award Abstract # 2112606
AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE)

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: OHIO STATE UNIVERSITY, THE
Initial Amendment Date: July 28, 2021
Latest Amendment Date: January 17, 2025
Award Number: 2112606
Award Instrument: Cooperative Agreement
Program Manager: Sheikh Ghafoor
sghafoor@nsf.gov
 (703)292-7116
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: November 1, 2021
End Date: October 31, 2026 (Estimated)
Total Intended Award Amount: $19,999,998.00
Total Awarded Amount to Date: $20,099,998.00
Funds Obligated to Date: FY 2021 = $11,999,998.00
FY 2022 = $100,000.00

FY 2023 = $8,000,000.00
History of Investigator:
  • Dhabaleswar Panda (Principal Investigator)
    panda.2@osu.edu
  • Vipin Chaudhary (Co-Principal Investigator)
  • Raghu Machiraju (Co-Principal Investigator)
  • Beth Plale (Co-Principal Investigator)
  • Eric Fosler-Lussier (Co-Principal Investigator)
Recipient Sponsored Research Office: Ohio State University
1960 KENNY RD
COLUMBUS
OH  US  43210-1016
(614)688-8735
Sponsor Congressional District: 03
Primary Place of Performance: Ohio State University
OH  US  43210-1063
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): DLWBSLWAJWR1
Parent UEI: MN4MDDMN8529
NSF Program(s): AI Research Institutes,
Information Technology Researc,
Special Projects - CNS
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01002526DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 120Z, 9102, 8004, 7231, 075Z
Program Element Code(s): 132Y00, 164000, 171400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Although the world is witness to the tremendous successes of Artificial Intelligence (AI) technologies in some domains, many domains have yet to reap the benefits of AI due to the lack of easily usable AI infrastructure. The NSF AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) will develop intelligent cyberinfrastructure with transparent and high-performance execution on diverse and heterogeneous environments. It will advance plug-and-play AI that is easy to use by scientists across a wide range of domains, promoting the democratization of AI. ICICLE brings together a multidisciplinary team of scientists and engineers, led by The Ohio State University in partnership with Case Western Reserve University, IC-FOODS, Indiana University, Iowa State University, Ohio Supercomputer Center, Rensselaer Polytechnic Institute, San Diego Supercomputer Center, Texas Advanced Computing Center, University of Utah, University of California-Davis, University of California-San Diego, University of Delaware, and University of Wisconsin-Madison. Initially, complex societal challenges in three use-inspired scientific domains will drive ICICLE?s research and workforce development agenda: Smart Foodsheds, Precision Agriculture, and Animal Ecology.

ICICLE?s research and development includes: (i) Empowering plug-and-play AI by advancing five foundational areas: knowledge graphs, model commons, adaptive AI, federated learning, and conversational AI. (ii) Providing a robust cyberinfrastructure capable of propelling AI-driven science (CI4AI), solving the challenges arising from heterogeneity in applications, software, and hardware, and disseminating the CI4AI innovations to use-inspired science domains. (iii) Creating new AI techniques for the adaptation/optimization of various CI components (AI4CI), enabling a virtuous cycle to advance both AI and CI. (iv) Developing novel techniques to address cross-cutting issues including privacy, accountability, and data integrity for CI and AI; and (v) Providing a geographically distributed and heterogeneous system consisting of software, data, and applications, orchestrated by a common application programming interface and execution middleware. ICICLE?s advanced and integrated edge, cloud, and high-performance computing hardware and software CI components simplify the use of AI, making it easier to address new areas of inquiry. In this way, ICICLE focuses on research in AI, innovation through AI, and accelerates the application of AI. ICICLE is building a diverse STEM workforce through innovative approaches to education, training, and broadening participation in computing that ensure sustained measurable outcomes and impact on a national scale, along the pipeline from middle/high school students to practitioners. As a nexus of collaboration, ICICLE promotes technology transfer to industry and other stakeholders, as well as data sharing and coordination across other National Science Foundation AI Institutes and Federal agencies. As a national resource for research, development, technology transfer, workforce development, and education, ICICLE is creating a widely usable, smarter, more robust and diverse, resilient, and effective CI4AI and AI4CI ecosystem.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 117)
Vallabhajoyula, Manikya S and Ramnath, R "Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems" , 2024 Citation Details
Akkas, Selahattin and Azad, Ariful "JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning" WWW '22: Companion Proceedings of the Web Conference 2022 , 2022 https://doi.org/10.1145/3487553.3524722 Citation Details
Akkas, Selahattin and Azad, Ariful ""JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning," in Companion Proceedings of the Web Conference," , 2022 Citation Details
Al-Attar, Kinan and Shafi, Aamir and Subramoni, Hari and Panda, Dhabaleswar K. "Towards Java-based HPC using the MVAPICH2 Library: Early Experiences" 2022 IEEE International Parallel and Distributed Processing Symposium Workshops ( , 2022 https://doi.org/10.1109/IPDPSW55747.2022.00091 Citation Details
Alnaasan, Nawras and Jain, Arpan and Shafi, Aamir and Subramoni, Hari and Panda, Dhabaleswar K "OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems" 23rd Parallel and Distributed Scientific and Engineering Computing Workshop (PDSEC) at IPDPS22 , 2022 https://doi.org/10.1109/IPDPSW55747.2022.00143 Citation Details
Alnaasan, Nawras and Lieber, Matthew and Shafi, Aamir and Subramoni, Hari and Shearer, Scott and Panda, Dhabaleswar K "HARVEST: High-Performance Artificial Vision Framework for Expert Labeling using Semi-Supervised Training" , 2023 https://doi.org/10.1109/BigData59044.2023.10386339 Citation Details
Anderson, Molly and Hoey, Lesli and Hurst, Peter and Miller, Michelle and Montenegro de Wit, Maywa "Debrief on the United Nations Food Systems Summit (UNFSS)" Journal of Agriculture, Food Systems, and Community Development , 2022 https://doi.org/10.5304/jafscd.2022.112.008 Citation Details
Averly, Reza and Chao, Wei-Lun "Unified Out-Of-Distribution Detection: A Model-Specific Perspective" , 2023 https://doi.org/10.1109/ICCV51070.2023.00140 Citation Details
Boubin, Jayson and Burley, Codi and Han, Peida and Li, Bowen and Porter, Barry and Stewart, Christopher "MARbLE: Multi-Agent Reinforcement Learning at the Edge for Digital Agriculture" SEC , 2022 https://doi.org/10.1109/SEC54971.2022.00013 Citation Details
Caglar_Oksuz, A and Halimi, A and Ayday, E "AUTOLYCUS: Exploiting Explainable Artificial Intelligence (XAI) for Model Extraction Attacks against Interpretable Models" , 2024 Citation Details
Chen, Chen-Chun and Khorassani, Kawthar Shafie and Anthony, Quentin G. and Shafi, Aamir and Subramoni, Hari and Panda, Dhabaleswar K. "Highly Efficient Alltoall and Alltoallv Communication Algorithms for GPU Systems" Heterogeneity in Computing Workshop , 2022 https://doi.org/10.1109/IPDPSW55747.2022.00014 Citation Details
(Showing: 1 - 10 of 117)

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