New ESnet Report Looks at AI-Powered Future for Scientific Networking
By Bonnie Powell, media@es.net
In February 2025, a group of network and software engineers, data scientists, and AI experts from both academia and industry gathered in Berkeley, California, for a two-and-a-half day deep dive into the future of scientific networking. The occasion was the Energy Sciences Network’s (ESnet’s) first-ever Data and AI Workshop — a focused effort to explore how data-driven methods paired with artificial intelligence/machine learning (AI/ML) could transform the way one of the world’s leading research networks operates.
And in July, ESnet released a 200-page report (https://doi.org/10.2172/2571672) summarizing the workshop’s findings and the organization’s next steps. The report’s resulting 29 work-packages and 31 recommendations set clear priorities in four focus areas — data management, traditional analytics, AI methods, and user experience.
It also underscores that trust and transparency are non‑negotiable. ESnet’s AI strategy prioritizes explainable systems, human oversight, ethical safeguards, and continuous feedback to ensure reliable, mission‑aligned operations.
