Topic: Data Science

On the Hidden in Plain Sight podcast, LLNL director Bill Goldstein explains how the Lab crunches data to shape the future.

Held in early March, the WiDS Livermore event featured four technical talks from LLNL data scientists.

The early-March event was the third annual WiDS Livermore event, featuring speakers, a career panel, mentoring, and a livestream.

LLNL has infrastructure, unique research capabilities, and a dedicated team of scientists and engineers supporting the fight against COVID-19.

LLNL scientists are contributing to the global fight against COVID-19 by combining AI/ML, bioinformatics, and supercomputing to help discover candidates for new antibodies and pharmaceutical drugs.

The university and LLNL lay the groundwork for a direct pipeline between the two, opening a door to research collaborations as well as job and internship opportunities for students and alumni.

Rafael Rivera-Soto is passionate about artificial intelligence, deep learning, and machine learning technologies. He works in LLNL’s Global Security Computing Applications Division, also known as GSCAD.

New year, new hackathon! The January 30–31 event was Computing’s 23rd hackathon and the 1st scheduled in the winter season.

Laser-fusion researchers have turned to machine-learning techniques to seek the combinations of laser pulse characteristics and target design needed to optimize target implosions for ICF.

A multi-institutional consortium aims to speed up the drug discovery pipeline by building predictive, data-driven pharmaceutical models.

More than 100 researchers from DOE national labs came to LLNL for the inaugural DOE Data Day workshop to discuss challenges and solutions in accessing, curating, and sharing data.

Livermore teams are applying innovative data analysis and interpretation techniques to advance fundamental science research.

LLNL researchers and colleagues are using machine learning as a virtual magnifying glass to study interesting regions of RAS protein/lipid simulations in higher detail.

ADAPD—Advanced Data Analytics for Proliferation Detection—integrates subject-matter expertise from the DOE’s national laboratories to create new capabilities for analyzing growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.

After years of preparation, LLNL’s upgraded Ares code runs a 98-billion-element simulation on 16,384 GPUs on the Sierra supercomputer.

Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.

Two LLNL computer scientists with promising technologies have taken part in a national organization’s commercialization program that pairs researchers with entrepreneurs.

LLNL teams conduct research using AI, and the Machine Learning Reading Group serves as a resource for employees to keep one another apprised of developments in this ever-changing field.

Cindy Gonzales earned a bachelor’s degree, started her master’s degree, and changed careers—all while working at the Lab. Meet one of our newest data scientists.

LLNL’s Data Science Institute hosted its second annual workshop with the University of California, emphasizing key challenges with machine learning and artificial intelligence in scientific research.

The partnership will apply DOE-fueled AI capabilities to advance transformative scientific opportunities in biomedical and public health research.

As part of the Department of Energy’s role in the fight against cancer, scientists are building tools that use supercomputers to solve problems in entirely new ways.

The Data Science Institute co-sponsored the women's lunch at the 2019 Conference on Knowledge Discovery and Data Mining. Alyson Fox and Amanda Minnich discussed LLNL's diversity and inclusion efforts.

The NNSA’s first exascale supercomputer, El Capitan, will have a peak performance of more than 1.5 exaflops (1.5 quintillion calculations per second) and an anticipated delivery in late 2022.

Highlights include perspectives on machine learning and artificial intelligence in science, data driven models, autonomous vehicle operations, and the OpenMP standard 5.0.