A computer cluster at Berkeley Lab, which switched off last month, since 1996 had served as a steady workhorse in supporting groundbreaking physics research conducted by large collaborations.
Researchers in Berkeley Lab’s Computational Research Division are applying deep learning and analytics to electronic health record (EHR) data to help the Veterans Administration address a host of medical and psychological challenges affecting many of the nation’s 700,000 military veterans.
Researchers at Berkeley Lab have turned parts of a 13,000-mile-long testbed of “dark fiber,” unused fiber-optic cable, owned by the DOE Energy Sciences Network (ESnet) into a highly sensitive seismic activity sensor that could potentially augment the performance of earthquake early warning systems currently being developed in the western United States.
Four college teams – UC Berkeley, UC Davis, Cal State University San Bernardino, and Embry-Riddle Aeronautical University – will square off at Berkeley Lab on Dec. 1 as part of DOE’s fourth collegiate CyberForce Competition. The event aims to address the cybersecurity capability gap and increase awareness around energy critical infrastructure.
Four Berkeley Lab scientists – Allen Goldstein, Sung-Hou Kim, Susannah Tringe, and Katherine Yelick – have been named Fellows of the American Association for the Advancement of Science (AAAS), the world’s largest general scientific society. They are among the 416 scientists awarded the distinction of AAAS Fellow this year.
A team of computational scientists and engineers from Berkeley Lab, Oak Ridge National Laboratory, and NVIDIA has been awarded the ACM Gordon Bell Prize for applying an exascale-class deep learning application to extreme climate data and breaking the exaop (1 billion billion calculations) computing barrier for the first time with a deep learning application.
In recognition of National Cybersecurity Awareness Month, cybersecurity expert Sean Peisert of Berkeley Lab discusses new methods that have the potential to keep our energy infrastructure safe from a cyberattack.
Researchers at Berkeley Lab are developing an “optimization algorithm” toolset that pinpoints which variables of a “black box” simulation model will churn out the most realistic data in less time.
Competing in a fictitious high-stakes scenario, a group of scientists at Berkeley Lab bested two dozen other teams in a months-long, data-driven scavenger hunt for simulated radioactive materials in a virtual urban environment.
The team behind Project Jupyter, an effort pioneered by Fernando Pérez, an assistant professor of statistics at UC Berkeley and staff scientist in the Usable Software Systems Group at Berkeley Lab’s Computational Research Division, has been honored with an Association of Computing Machinery Software System Award for developing a tool that has had a lasting influence on computing.