Topic: Data Science

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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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.

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

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Alan Noun, a computer science student at CSU East Bay and the recipient of the Livermore Lab Foundation's first full-year scholarship, is an intern at LLNL’s Data Science Summer Institute.

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Ryan Chen, LLNL data analyst and visualization technologist, has developed a model called the RDD Studio that provides a detailed simulation of an optimal response to a radiological dispersal device.

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Simulation workflows for Arbitrary Lagrangian–Eulerian (ALE) methods are highly complex and often require a manual tuning process. There is an urgent need to semi-automate this process to reduce user burden and improve productivity. To address this need, we are developing novel predictive analytics for simulations and an in situ infrastructure for integration of analytics. Our ongoing goals are to predict simulation failures ahead of time and proactively avoid them as much as possible.

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LLNL recently hosted its first-ever Data Science Challenge Workshop, a two-week crash course in what it’s like to work in data science, with students from UC Merced.

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Brothers and Computation teammates Joe and Sam Eklund discuss their multi-hackathon project using Deep Voice 3.

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Fred Streitz explains LLNL's work to exploit the relationship between simulation and experiments to build predictive codes.

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As demonstrated by CASC computer scientists, LLNL's innovative data-driven machine learning techniques teach computers to solve real-world problems.

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According to DOE secretary Rick Perry, "Accelerating artificial intelligence and machine learning is crucial to strengthening our country’s economic and national security."

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LLNL’s Center for Applied Scientific Computing looks back at 2018 papers, presentations, and other activities recognizing research and innovation in data science.

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For the 2nd straight year, LLNL's HPC Innovation Center hosted to a Women in Data Science (WiDS) regional event, drawing dozens of attendees from LLNL, local universities, and other Bay Area labs.

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With nearly 100 publications, CASC researcher Jayaraman “Jay” Thiagarajan explores the possibilities of artificial intelligence and machine learning technologies.

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Held in Washington, DC, the Earth System Grid Federation’s 8th annual face-to-face conference was a lively, fruitful affair.

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LLNL employees attended a five-part “Deep Learning 101” course, which introduced the basics of neural networks and machine learning to anyone with a basic knowledge of programming in Python.

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Highlights include CASC director Jeff Hittinger's vision for the center as well as recent work with PruneJuice DataRaceBench, Caliper, and SUNDIALS.

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Rushil Anirudh describes the machine learning field as undergoing a “gold rush.”

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