More than 100 million smart meters have been installed in the U.S. to record and communicate electric consumption, voltage, and current to consumers and grid operators. LLNL has developed GridDS to help make the most of this data.
An LLNL team will be among the first researchers to perform work on the world’s first exascale supercomputer—Oak Ridge National Laboratory’s Frontier—when they use the system to model cancer-causing protein mutations.
The Accelerating Therapeutic Opportunities in Medicine (ATOM) consortium is showing “significant” progress in demonstrating that HPC and machine learning tools can speed up the drug discovery process, ATOM co-lead Jim Brase said at a recent webinar.
Winning the best paper award at PacificVis 2022, a research team has developed a resolution-precision-adaptive representation technique that reduces mesh sizes, thereby reducing the memory and storage footprints of large scientific datasets.
Technologies developed through the Next-Generation High Performance Computing Network project are expected to support mission-critical applications for HPC, AI and ML, and high performance data analytics. Applications could include stockpile stewardship, fusion research, advanced manufacturing, climate research and other open science on future ASC HPC systems.
Sponsored by the DSI, LLNL’s winter hackathon took place on February 16–17. In addition to traditional hacking, the hackathon included a special datathon competition in anticipation of the Women in Data Science (WiDS) conference on March 7.
LLNL researchers and collaborators have developed a highly detailed, ML–backed multiscale model revealing the importance of lipids to RAS, a family of proteins whose mutations are linked to many cancers.
LLNL will lend its expertise in vaccine research and computing resources to the Human Vaccines Project consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.
Brian Gallagher works on applications of machine learning for a variety of science and national security questions. He’s also a group leader, student mentor, and the new director of LLNL’s Data Science Challenge.
The ADAPD program held a virtual meeting to highlight science-based, data-driven analysis work to advance AI innovation and AI-enabled systems to enhance the U.S. nuclear proliferation detection activities.
LLNL is looking for participants and attendees from industry, research institutions and academia for the first-ever Machine Learning for Industry Forum (ML4I), a three-day virtual event starting Aug. 10.