Topic: Data Science

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

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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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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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An LLNL-led team 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.

Project

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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This summer, the Computing Scholar Program welcomed 160 undergraduate and graduate students into virtual internships. The Lab’s open-source community was already primed for student participation.

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Computing’s summer hackathon was held virtually on August 6–7 and featured presentations from teams who tested software technologies, expanded project features, or explored new ways of analyzing data.

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Computing’s fourth annual Developer Day was held as a virtual event on July 30 with 8 speakers and 90 participants.

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LLNL and Cerebras Systems have installed the company’s AI computer into Lassen, making LLNL the first institution to integrate the cutting-edge AI platform with a supercomputer.

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This video provides an overview of LLNL projects in which data scientists work with domain scientists to address major challenges in healthcare.

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Ian Karlin on AI hardware integration into HPC systems, workflows, followed by a talk about software integration of AI accelerators in HPC with Brian Van Essen.

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Lawrence Livermore National Lab has named Stefanie Guenther as Computing’s fourth Sidney Fernbach Postdoctoral Fellow in the Computing Sciences. This highly competitive fellowship is named after LLNL’s former Director of Computation and is awarded to exceptional candidates who demonstrate the potential for significant achievements in computational mathematics, computer science, data science, or scientific computing.

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Two papers featuring LLNL scientists were accepted in the 2020 International Conference on Machine Learning (ICML), one of the world’s premier conferences of its kind.

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In this year's Data Science Challenge with UC Merced, 21 students developed machine learning models capable of differentiating potentially explosive materials from other types of molecules.

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Surrogate models supported by neural networks could lead to new insights in complicated physics problems such as inertial confinement fusion.

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An LLNL-led team proposes a DL approach aimed at improving the reliability of classifier models for predicting disease types from diagnostic images.

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