Topic: Data Science

In this video from the Stanford HPC Conference, Katie Lewis presents "The Incorporation of Machine Learning into Scientific Simulations at Lawrence Livermore National Laboratory."

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LLNL researchers have identified an initial set of therapeutic antibody sequences, designed in a few weeks using machine learning and supercomputing, aimed at binding and neutralizing SARS-CoV-2.

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

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

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

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

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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.

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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.

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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.

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

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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.

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

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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.

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

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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.

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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.

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

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

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

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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.

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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.

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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.

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

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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.

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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.

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