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, THE
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: 01002122DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT

01002526DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9102, 7231, 120Z, 075Z, 8004
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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Halimi, Anisa and Dervishi, Leonard and Ayday*, Erman and Pyrgelis, Apostolos and Troncoso-Pastoriza, Juan Ramรณn and Hubaux, Jean-Pierre and Jiang, Xiaoqian and Vaidya, Jaideep "Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies" Proceedings on Privacy Enhancing Technologies , 2022 Citation Details
Irizarry, Kevyn Angueira and Stewart, Christopher "Profiling Edge Resource Demands of Zoom Maneuvers for Autonomous Unmanned Aerial Vehicles" , 2023 Citation Details
Islam, Maliha T and Fariha, A and Meliou, A and Salimi, B "Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification" , 2024 Citation Details
Islam, Taimoor Ul and Zhang, Tianyi and Boateng, Joshua Ofori and Gossling, Evan and Zu, Guoying and Babu, Sarath and Zhang, Hongwei and Qiao, Daji "AraMIMO: Programmable TVWS mMIMO Living Lab for Rural Wireless" , 2023 https://doi.org/10.1145/3615453.3616512 Citation Details
Jain, A and Shafi, A. and Anthony, Q. and Kousha, P. and Subramoni, H. and Panda, DK. "Hy-Fi: Hybrid Five-Dimensional Parallel DNN Training on High-Performance GPU Clusters" Proceedings International Conference on High Performance Computing , 2022 https://doi.org/10.1007/978-3-031-07312-0_6 Citation Details
Jiang, Yuzhou and Ji, Tianxi and Ayday, Erman "Reproducibility-Oriented and Privacy-Preserving Genomic Dataset Sharing" , 2023 Citation Details
Jiang, Yuzhou and Yilmaz, Emre and Ayday, Erman "Robust Fingerprint of Location Trajectories Under Differential Privacy" , 2023 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
(Showing: 1 - 10 of 117)

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