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Researchers at Stanford University, the European Center for Medium-Range Weather Forecasts, or ECMWF, and ORNL used the lab’s Summit supercomputer to better understand atmospheric gravity waves, which influence significant weather patterns that are difficult to forecast.

From during his early years at NASA to his current role a researcher and group leader, Peter Fuhr has pushed the boundaries of optical and sensor technology. Fuhr’s path is marked by wacky creativity that can’t confine itself to challenges in a single field. No idea is too far out to try out — and so many of them work that Fuhr has a host of inventions and start-ups under his belt.

Joel Brogan, who leads the Multimodal Sensor Analytics group at Oak Ridge National Laboratory, has been elevated to senior membership in the Institute of Electrical and Electronics Engineers.

A paper written by researchers from the Department of Energy’s Oak Ridge National Laboratory was selected as the top paper of 2023 by Welding Journal that explored the feasibility of using laser-blown powder direct energy deposition, or Laser-powder DED.

The ForWarn visualization tool was co-developed by ORNL with the U.S. Forest Service. The tool captures and analyzes satellite imagery to track impacts such as storms, wildfire and pests on forests across the nation.

ORNL has been recognized in the 21st edition of the HPCwire Readers’ and Editors’ Choice Awards, presented at the 2024 International Conference for High Performance Computing, Networking, Storage and Analysis in Atlanta, Georgia.

Researchers used the world’s fastest supercomputer, Frontier, to train an AI model that designs proteins, with applications in fields like vaccines, cancer treatments, and environmental bioremediation. The study earned a finalist nomination for the Gordon Bell Prize, recognizing innovation in high-performance computing for science.

Researchers at Oak Ridge National Laboratory used the Frontier supercomputer to train the world’s largest AI model for weather prediction, paving the way for hyperlocal, ultra-accurate forecasts. This achievement earned them a finalist nomination for the prestigious Gordon Bell Prize for Climate Modeling.

A research team led by the University of Maryland has been nominated for the Association for Computing Machinery’s Gordon Bell Prize. The team is being recognized for developing a scalable, distributed training framework called AxoNN, which leverages GPUs to rapidly train large language models.

Aditya Sundararajan of ORNL’s Grid Systems Architecture group was elevated to senior status within the Institute of Electrical and Electronics Engineers.