Though Nell Barber wasn’t sure what her future held after graduating with a bachelor’s degree in psychology, she now uses her interest in human behavior to design systems that leverage machine learning algorithms to identify faces in a crowd.
Steven Arndt, distinguished R&D staff member in the Nuclear Energy and Fuel Cycle Division at ORNL, began a one-year term on June 16 as the 68th President of the American Nuclear Society.
Two decades in the making, a new flagship facility for nuclear physics opened on May 2, and scientists from the Department of Energy’s Oak Ridge National Laboratory have a hand in 10 of its first 34 experiments.
The Frontier supercomputer at the Department of Energy’s Oak Ridge National Laboratory earned the top ranking today as the world’s fastest on the 59th TOP500 list, with 1.1 exaflops of performance. The system is the first to achieve an unprecedented level of computing performance known as exascale, a threshold of a quintillion calculations per second.
Friederike (Rike) Bostelmann, who began her career in Germany, chose to come to ORNL to become part of the Lab’s efforts to shape the future of nuclear energy.
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
To achieve practical energy from fusion, extreme heat from the fusion system “blanket” component must be extracted safely and efficiently. ORNL fusion experts are exploring how tiny 3D-printed obstacles placed inside the narrow pipes of a custom-made cooling system could be a solution for removing heat from the blanket.
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.