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ORNL hosted the second annual Appalachian Carbon Forum in Lexington March 7-8, 2024, where ORNL and University of Kentucky’s Center for Applied Energy Research scientists led discussions with representatives from

While government regulations are slowly coming, a group of cybersecurity professionals are sharing best practices to protect large language models powering these tools. Sean Oesch, a leader in emerging cyber technologies, recently contributed to the OWASP AI Security and Privacy Guide to inform global AI security standards and regulations.

In partnership with the National Cancer Institute, researchers from the Department of Energy’s Oak Ridge National Laboratory’s Modeling Outcomes for Surveillance using Scalable Artificial Intelligence are building on their groundbreaking work to

Anuj J. Kapadia, who heads the Advanced Computing Methods for Health Sciences Section at ORNL, has been elected as president of the Southeastern Chapter of the American Association of Physicists in Medicine. 

To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance. 

ORNL scientists and researchers attended the annual American Geophysical Union meeting and came away inspired for the year ahead in geospatial, earth and climate science. 

ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.

Rigoberto “Gobet” Advincula, a scientist at the Department of Energy’s Oak Ridge National Laboratory, has been appointed a Fellow of the Institute of Materials, Minerals and Mining.

Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.

Researchers at the Department of Energy’s Oak Ridge and Lawrence Berkeley National Laboratories are evolving graph neural networks to scale on the nation’s most powerful computational resources, a necessary step in tackling today’s data-centric