Ferreira Da Silva, Rafael ; Klasky, Scott A; Patton, Robert M; Pugmire, David R; Thomas, Todd M; Widener, Patrick M; Athawale, Tushar M; Bhowmick, Chandreyee ; Chae, Junghoon ; Choi, Jong Youl ; Coletti, Mark A; Cong, Guojing ; Das, Sanjay ; Date, Prasanna A; Dunivan Stahl, Chelsey K; Engstrom, Katherine A; Gautam, Ashish ; Gong, Qian ; Johnson-Scott, Zachary L; Kim, Minsu ; Kotevska, Olivera ; Lee, Jaemoon ; Li, Zhimin ; Logan, Jeremy S; Mehta, Kshitij V; Moreland, Kenneth D; Podhorszki, Norbert ; Schmidt, Erik H; Suchyta, Eric D; Suter, Frederic
Publication Date:
September 24, 2026
Abstract
The Data and AI Systems Section of the Computer Science and Mathematics Division at Oak Ridge National Laboratory conducts research on the data and artificial intelligence foundations of scientific discovery, shortening and automating the path from instrument and simulation to insight at the scale of leadership computing facilities. This report collects 27 project highlights from fiscal year 2026, organized by the section's five groups, namely Data Engineering, Learning Systems, Performance Engineering, Visualization, and Workflow Systems, each introduced by its group leader. Every highlight is written by the people who did the work and names someone who can be contacted about it directly. The highlights cover AI methods co-designed with the platforms that run them, from spiking and neuromorphic models to energy-efficient large language models. They also cover data reduction and campaign management that make scientific datasets AI-ready, uncertainty-aware and AI-assisted visualization at leadership scale, and workflows that couple experiment, simulation, and AI into closed loops needing little human intervention. Reported applications span fusion energy, materials science, electron microscopy, cosmology, plant phenotyping, health analytics, and national security. Much of our current agenda runs through the Department of Energy's Genesis Mission, where we contribute to the American Science Cloud and to the Transformational AI Models Consortium.
