Topic: Data Science

LLNL researchers have developed a novel machine learning (ML) model that can predict 10 distinct polymer properties more accurately than was possible with previous ML models.

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The 2022 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22) returned to Dallas as a large contingent of LLNL staff participated in sessions, panels, paper presentations and workshops centered around HPC.

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High-precision numerical data from computer simulations, observations, and experiments is often represented in floating point and can easily reach terabytes to petabytes of storage.

Project

The award recognizes progress in the team's ML-based approach to modeling ICF experiments, which has led to the creation of faster and more accurate models of ICF implosions.

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Two LLNL-led teams received SciVis Test of Time awards at the 2022 IEEE VIS conference for papers that have achieved lasting relevancy in the field of scientific visualization.

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In a time-trial competition, participants trained an autonomous race car with reinforcement learning algorithms.

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Researchers are starting a three-year project aimed at improving methods for visual analysis of large heterogeneous datasets as part of a recent DOE funding opportunity.

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The Earth System Grid Federation, a multi-agency initiative that gathers and distributes data for top-tier projections of the Earth’s climate, is preparing a series of upgrades to make using the data easier and faster while improving how the information is curated.

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The Earth System Grid Federation is a web-based tool set that powers most global climate change research.

Project

The second article in a series about the Lab's stockpile stewardship mission highlights computational models, parallel architectures, and data science techniques.

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The new oneAPI Center of Excellence will involve the Center for Applied Scientific Computing and accelerate ZFP compression software to advance exascale computing.

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The Adaptive Computing Environment and Simulations (ACES) project will advance fissile materials production models and reduce risk of nuclear proliferation.

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After 10 years and 33 hackathons, nothing can stop this beloved tradition.

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LLNL participates in the CMD-IT/ACM Richard Tapia Celebration of Diversity in Computing Conference (Tapia2022) on September 7–10.

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zfp is an open-source C/C++ library for compressed floating-point and integer arrays that support high throughput read and write random access.

Project

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.

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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.

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The Data Science Institute's career panel series continued on June 28 with a discussion of LLNL’s COVID-19 research and development. Four data scientists talked about their work in drug screening, protein–drug compounds, antibody–antigen sequence analysis, and risk factor identification.

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Angeline Lee simultaneously serves as a group leader, contributes to programmatic projects, and studies for her bachelor’s degree.

People Highlight

For the first time in the DSC series since the COVID-19 pandemic began in 2020, Lab mentors visited the college campus to provide in-person guidance for five teams of UC Merced students.

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Livermore’s machine learning experts aim to provide assurances on performance and enable trust in machine-learning technology through innovative validation and verification techniques.

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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.

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LLNL participates in the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 through June 3.

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