SDSC and the Wisconsin IceCube Particle Astrophysics Center (WIPAC) successfully completed a computational experiment as part of a multi-institution collaboration that marshalled all globally available for sale GPUs (graphics processing units) across Amazon Web Services, Microsoft, and Google.
San Diego-based Predictive Science, Inc. this week released their first forecast for the 2019-2020 influenza season, which typically runs from November through March.
Researchers at the San Diego Supercomputer Center at UC San Diego have launched an open-source software called SeedMeLab, which provides a host of features for researchers across all disciplines to manage and disseminate their data.
MIT’s Computer Science & Artificial Intelligence Laboratory (CSAIL) and the Center for Applied Internet Data Analysis (CAIDA) at SDSC have developed a new machine learning system to identify "serial hijackers" of internet IP addresses.
KC Claffy, director of the Center for Applied Data Analysis (CAIDA) at the University of California’s San Diego Supercomputer Center, has been inducted into the Internet Hall of Fame for her pioneering work in the area of internet measurement and analysis.
Researchers at SDSC and UC San Diego School of Medicine have received two National Science Foundation (NSF) planning grants worth a combined $2 million to invest in research collaborations between academia, industry, government, and communities.
University of California San Diego has been awarded $4.6 million from the National Institute of Mental Health (NIMH) to create the Neuroelectromagnetic Data Archive and Tools Resource (NEMAR).
Researchers used SDSC's Comet supercomputer to better understand the wake effects of large floating wind farm arrays, which have become more prevalent in recent years.
The NSF has awarded SDSC and its partners a three-year, $5.9 million grant to host the EarthCube Office as part of the ongoing NSF-funded EarthCube program.
Researchers at SDSC, LANL, and UNC Chapel Hill have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds.