Building Expertise, Connections, and Communities for Computational AI and HPC Training and Education: NAIRR Pilot User Experience Group Initiatives

Volume 17, Issue 1 (March 2026), pp. 70–74

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@article{jocse-17-1-11,
  author={Nitin Sukhija and Shelley Knuth and Alana Romanella and Marisa Brazil},
  title={Building Expertise, Connections, and Communities for Computational AI and HPC Training and Education: NAIRR Pilot User Experience Group Initiatives},
  journal={The Journal of Computational Science Education},
  year=2026,
  month=mar,
  volume=17,
  issue=1,
  pages={70--74},
  doi={https://doi.org/10.22369/issn.2153-4136/17/1/11}
}

Given the rapidly changing computing landscape propelled with innovations and convergence of new cutting-edge technologies such as high-performance computing (HPC), AI, Cybersecurity, Quantum computing and more, the accelerated need for upskilling/ reskilling the workforce to mitigate skills gaps is becoming increasingly important. Whether you are student, researcher, faculty, staff, or other stakeholder of academia/industry who is part of this evolving digital ecosystem, the continuous learning and adaptation of HPC along with AI best practices, research and technology is a key to remain competitive. Furthermore, a triumvirate of user expertise, connections, and communities is required to enable efficient integration of (HPC) and AI ecosystem to offer key technologies for meeting performance requirements that pushes innovations to their limits in science, engineering and other domains. To address the challenges involved in leveraging Artificial Intelligence (AI) along with computational, data, software, training, and educational resources for the U.S. research and education communities, the National Artificial Intelligence Research Resource (NAIRR) Pilot was launched in 2024. As part of this effort, the NAIRR Pilot User Experience Working Group (UEWG) have conducted various engagement initiates, such as researcher showcases, pilot industry partner showcases, webinar series, regional workshops and one national workshop on AI Training. This paper presents a reproducible roadmap based on the observations and results of the above-mentioned training and education efforts that can be used to efficiently train the next generation workforce in AI and HPC at all levels. Thus, bridging the talent gap and advancing secure and trustworthy AI in research and society.