Topic: Data Science

This project aims to tackle the complexities of HPC software integration with dependency compatibility models, binary analysis tools, efficient logic solvers, and configuration optimization techniques.

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Led by computational scientist Youngsoo Choi, the Data-Driven Physical Simulation reading group has been meeting biweekly since October 2019. The pandemic almost disbanded the group... until it turned into a virtual seminar series.

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In his opening keynote address at the AI Systems Summit, LLNL CTO Bronis de Supinski described integration of two AI-specific systems to achieve system level heterogeneity.

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In recognition of March as International Women’s History Month, SC21 profiled six women doing trailblazing work, including LLNL's Hiranmayi Ranganathan.

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The Accelerating Therapeutics for Opportunities in Medicine consortium, of which LLNL is part, announced the U.S. Department of Energy’s Argonne, Brookhaven and Oak Ridge national labs are joining the consortium to further develop ATOM’s AI-driven drug discovery platform.

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The Data Science Institute sponsored LLNL’s 27th hackathon on February 11–12. Organizers offered a deep learning tutorial and presentations showcasing data science techniques.

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Coinciding with International Women’s Day on March 8, LLNL’s 4th Women in Data Science (WiDS) regional event brought women together to discuss successes, opportunities and challenges of being female in a mostly male field.

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LLNL's Ana Kupresanin, CASC deputy director and member of the Data Science Institute council, was recently featured in a Frontiers of Engineering alumni spotlight. FOE is run by the National Academy of Engineering nonprofit organization.

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LLNL and IBM research on deep learning models to accurately diagnose diseases from x-ray images won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference.

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As part of the 50th anniversary of Virginia Tech’s computer science department, the university is featuring active and dynamic alumni—including LLNL computer scientist Ghaleb Abdulla.

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Our researchers will be well represented at the virtual SIAM Conference on Computational Science and Engineering (CSE21) on March 1–5. SIAM is the Society for Industrial and Applied Mathematics with an international community of more than 14,500 individual members.

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An LLNL team has developed a “Learn-by-Calibrating” method for creating powerful scientific emulators that could be used as proxies for far more computationally intensive simulators.

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Three papers address feature importance estimation under distribution shifts, attribute-guided adversarial training, and uncertainty matching in graph neural networks.

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StarSapphire is a collection of scientific data mining projects focusing on the analysis of data from scientific simulations, observations, and experiments.

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A team led by LLNL developed a new kind of prior—a characterization of the space of natural images—for compressive image recovery that is trained on patches of images instead of full-sized images.

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The 34th Conference on Neural Information Processing Systems features two papers advancing the reliability of deep learning for mission-critical applications at LLNL.

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LLNL will collaborate with Machina Labs to apply ML to aluminum sheet metal processing for aerospace and automotive applications. Five recently announced LLNL-led projects will be funded by HPC4EI.

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fpzip is a library for lossless or lossy compression of multidimensional floating-point arrays. It was primarily designed for lossless compression.

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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.

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Nisha Mulakken is advancing COVID-19 R&D and mentoring the next generation. “The opportunities we are exposed to early in our careers can shape the limits we place on ourselves and our approaches to challenges we encounter throughout our careers,” she says.

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LLNL’s Computing Directorate heads to the 32nd annual Supercomputing Conference (SC20) held virtually on November 9–19. Although the format is different this year, we’re turning out in full force.

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LLNL participates in the 32nd annual Supercomputing Conference (SC20) held virtually on November 9–19, 2020.

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LLNL has installed a new AI accelerator into the Corona supercomputer, allowing researchers to run simulations while offloading AI calculations from those simulations to the AI system.

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Livermore Computing's CTO Bronis de Supinski discusses the Lab's early work with, and vision for, the Cerebras-Lassen hardware integration.

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CASC researcher Harsh Bhatia thrives in the Lab’s versatile research environment. “At the Lab, no two problems are the same. Therefore, as a team, researchers deliver hundreds of new data science solutions each year. We are very fortunate to have access to many high-impact projects so we can really make a difference with our data science or data analysis solutions," he says.

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