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Led by The Ohio State University, the National Science Foundation funded AI institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) will build the next generation of Cyberinfrastructure to render Artificial Intelligence (AI) more accessible to everyone and drive its further democratization in the larger society.
The widespread adoption of Artificial Intelligence (AI) fueling advances in science, education, and commerce has been driven not only by the ability to aggregate data from a wide range of sources, but also by the availability of increasingly powerful Cyberinfrastructure (CI) supporting AI advances. As CI becomes more complex and heterogeneous, end users of the technology face a bewildering set of choices in applying AI to leverage insightful analytics, modeling complex systems, or enabling automation. 
The following video provides a high-level overview and rationale of the ICICLE project.

Mission Statement 

As a national infrastructure that enables artificial intelligence at the flick of a switch, ICICLE will transform today’s AI landscape from a narrow set of privileged disciplines to one where democratized AI empowers domains broadly through integrated plug-and-play AI. Converging under one virtual roof, ICICLE will foster interdisciplinary communities, advance foundational AI and CI, and transform application domains. Through its innovative approach to training and technology transfer, ICICLE will grow an AI-enabled workforce and incubate innovative companies with sustained diversity and inclusion at all levels. Ultimately, ICICLE will enable a transparent and trustworthy national infrastructure for an AI-enabled future to address pressing societal problems and enable decision-making for national priorities.
Main Thrusts

Project Components and Stakeholders

The project team recognizes a massive and ever-growing gap between available AI techniques and their availability to end users. 

Key Value Propositions of ICICLE

  • Establish a national Cyberinfrastructure for AI
  • Develop rubrics for training next generation of researchers who can translate from use cases to AI-powered CI
  • Integrate emerging AI technologies with advanced CI capabilities
  • Design a roadmap for future AI-driven science and Cyberinfrastructure
  • Build a nexus of collaboration among AI, CI, and domain sciences

ICICLE Ethics Statement

Developed by a consortium of researchers, educators, and community leaders from Artificial Intelligence (AI), cyberinfrastructure (CI), and food systems, precision agriculture, and animal ecology domains across fourteen institutions, ICICLE employs an edge-to-center, plug and play model that builds trustworthiness into the system by leveraging domain knowledge to facilitate contextual, sustainable, and democratizing outcomes. Democratizing AI demands not only equitable access, but trustworthiness in practice and research. This is performed at ICICLE’s Institutional Level through team-wide workforce development training activities in unconscious bias and bias in data and models; through the implementation of a project-wide, auditable (transparent and reproducible) workflows; and through the co-design of field edge-to-HPC/Cloud infrastructure.  And, it is executed at the AI/System Level through data integrity and privacy preserving practices, and through an ontology-driven system architecture focused on traceable, conversational, and graphical explainability. 

Project Partners


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News and Events
Presentation by Ashish Hingle, ICICLE class of 2023 Educational Fellow
ICICLE & AIIRA sign MOU to strengthen collaboration
ICICLE Researcher, Prof. Zhao Zhang from Rutgers, receives an NSF CAREER Award.
The ICICLE Project is now accepting applications for its 2024 cohort of educational fellows
OSC hosts ICICLE meeting, planning for year three of artificial intelligence project
NSF AI Institutes for Advances in Optimization (AI4OPT) and Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) sign MOU to strengthen collaboration
ICICLE at PEARC 2023 – Computing for the Common Good

Case Western University
Case Western Reserve University
Indiana Univeristy
Indiana University
Iowa State University
Iowa State University
Ohio Supercomputer Center
Ohio State University
Rensselaer Polytechnic Institute
Rutgers University
San Diego Supercomputer Center
Texas Advanced Computing Center
University of Arizona
University of Arizona
UC Davis
University of California, Davis
UC San Diego
University of California, San Diego
University of Delaware
University of Delaware
University of Utah
University of Wisconsin
University of Wisconsin