Topic: Data Science

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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Rafael Rivera-Soto is passionate about artificial intelligence, deep learning, and machine learning technologies. He works in LLNL’s Global Security Computing Applications Division, also known as GSCAD.

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ADAPD integrates expertise from DOE national labs to analyze growing global data streams and traditional intelligence data, enabling early warning of nuclear proliferation activities.

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Researchers develop innovative data representations and algorithms to provide faster, more efficient ways to preserve information encoded in data.

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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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Simulation workflows for ALE methods often require a manual tuning process. We are developing novel predictive analytics for simulations and an infrastructure for integration of analytics.

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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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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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AIMS (Analytics and Informatics Management Systems) develops integrated cyberinfrastructure for big climate data discovery, analytics, simulations, and knowledge innovation.

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Marisa Torres, software developer with LLNL’s Global Security Computing Applications Division, combines her love of biology with coding.

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Highlights include the directorate's annual external review, machine learning for ALE simulations, CFD modeling for low-carbon solutions, seismic modeling, and an in-line floating point compression tool.

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SOAR (Stateless, One-pass Adaptive Refinement) is a view-dependent mesh refinement and rendering algorithm.

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Highlights include the HYPRE library, recent data science efforts, the IDEALS project, and the latest on the Exascale Computing Project.

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At just 5 years old, Marisol Gamboa, the oldest of six siblings to Mexican immigrants, decided she was definitely going to college.

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Newly developed mathematical techniques reveal important tools for data mining analysis.

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Drawing from data mining, image and video processing, statistics, and pattern recognition, these computational tools improve the way scientists extract useful information from data.

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This project's techniques reduce bandwidth requirements for large unstructured data by making use of data compression and optimizing the layout of the data for better locality and cache reuse.

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New platforms are improving big data computing on Livermore’s high performance computers.

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LLNL computer scientists use machine learning to model and characterize the performance and ultimately accelerate the development of adaptive applications.

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Researchers are developing enhanced computed tomography image processing methods for explosives identification and other national security applications.

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LLNL and University of Utah researchers have developed an advanced, intuitive method for analyzing and visualizing complex data sets.

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hzip 1.0.1 is a C++ library for lossless compression of structured and unstructured meshes composed of cells with hypercube topology.

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The flourishing of simulation-based scientific discovery has also resulted in the emergence of the UQ discipline, which is essential for validating and verifying computer models.

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