Topic: Data Science

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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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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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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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.

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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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LLNL and the United Kingdom’s Hartree Centre are launching a new webinar series intended to spur collaboration with industry through discussions on computational science, HPC, and data science.

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Kevin McLoughlin has always been fascinated by the intersection of computing and biology. His LLNL career encompasses award-winning microbial detection technology, a COVID-19 antiviral drug design pipeline, and work with the ATOM consortium.

People Highlight

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.

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The Exascale Computing Project (ECP) 2022 Community Birds-of-a-Feather Days will take place May 10–12 via Zoom. The event provides an opportunity for the HPC community to engage with ECP teams to discuss our latest development efforts.

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LLNL celebrated the 2022 Global Women in Data Science conference on March 7 with its 5th annual regional event, featuring workshops, mentoring sessions and a discussion with LLNL Director Kim Budil.

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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.

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Registration is open until February 27 for LLNL's fifth annual WiDS event in conjunction with the worldwide Women in Data Science conference.

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The Department of Energy's Office of Science interviewed LLNL computer scientist Peter Lindstrom about his work since receiving the 2011 Early Career Award.

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From molecular screening, a software platform, and an online data to the computing systems that power these projects.

Project

LLNL’s cyber programs work across a broad sponsor space to develop technologies addressing sophisticated cyber threats directed at national security and civilian critical infrastructure.

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LC sited two different AI accelerators in 2020: the Cerebras wafer-scale AI engine attached to Lassen; and an AI accelerator from SambaNova Systems into the Corona cluster.

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This project advances research in physics-informed ML, invests in validated and explainable ML, creates an advanced data environment, builds ML expertise across the complex, and more.

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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.

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LLNL has established the AI Innovation Incubator (AI3), a collaborative hub aimed at uniting experts from LLNL, industry, and academia to advance AI for scientific and commercial applications.

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For the first time ever, SC21 went hybrid, with dozens of both in-person and virtual workshops, technical paper presentations, panels, tutorials and “birds of a feather” sessions.

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The Data Science Institute hosted a career panel featuring members of some of LLNL's Employee Resource Groups: Asian Pacific American Council, Amigos Unidos Hispanics in Partnership, Women’s Association, and Abilities Champions.

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LLNL is participating in the 33rd annual Supercomputing Conference (SC21), which will be held both virtually and in St. Louis on November 14–19, 2021.

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An LLNL mathematician and collaborators have developed a machine learning–based technique capable of deriving a mathematical model for the motion of binary black holes from gravitational wave data.

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