Topic: Data Science

The Center for Applied Scientific Computing and Data Science Institute welcomed a new academic partner to the 2021 Data Science Challenge program: the University of California Riverside campus. The intensive program has run for three years with UC Merced, and it tasks students with addressing a real-world scientific problem using data science techniques.

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Computational biology is using HPC to rapidly design and develop ways to treat cancer and COVID. LLNL researcher Felice Lightstone discusses ATOM (Accelerated Therapeutic Opportunities in Medicine) in this edition of SC21 TV.

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An LLNL-led effort in data compression was one of nine projects recently funded by the DOE for research aimed at shrinking the amount of data needed to advance scientific discovery. Under the project — ComPRESS: Compression and Progressive Retrieval for Exascale Simulations and Sensors — LLNL scientists will seek better understanding of data-compression errors.

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LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on August 10–12. Co-hosted by the Lab’s High-Performance Computing Innovation Center and Data Science Institute, the virtual event brought together more than 500 attendees from the Department of Energy (DOE) complex, commercial companies, professional societies, and academia.

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The Livermore-led VisIt visualization and analysis tool has supported scalable, high-quality evaluation of simulation results for over 20 years.

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From studying radioactive isotope effects to better understanding cancer metastasis, the Laboratory’s relationship with cancer research endures some 60 years after it began, with historical precedent underpinning exciting new research areas.

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A new career panel series that kicked off in June continued on August 10 with a session featuring former LLNL interns who converted to full-time employment at the Lab. Moderator Mary Silva was joined by panelists from the Computing and Engineering Directorates.

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LLNL and Purdue are partnering to speed up drug design using computational tools under the Accelerating Therapeutic Opportunities in Medicine project. LLNL researcher Jonathan Allen mentored students and two teaching assistants, introducing them to computationally driven drug discovery and designing predictive models for drug candidates.

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Meeting virtually three times per week, 22 UC Merced students engaged with LLNL mentors and peers to address a real-world challenge problem, using machine learning to identify potentially hazardous asteroids that could pose an existential threat to humanity.

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Brian Gallagher works on applications of machine learning for a variety of science and national security questions. He’s also a group leader, student mentor, and the new director of LLNL’s Data Science Challenge.

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On a recent video episode of The Data Standard Podcast, biostatistician Nisha Mulakken discusses the Lawrence Livermore Microbial Detection Array (LLMDA) system, which has detection capability for all variants of SARS-CoV-2.

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The Data Science Institute's seminar series has transitioned to a virtual format, and a playlist of recently recorded seminars is available on the Livermore Lab Events YouTube channel.

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The 2021 Conference on Computer Vision and Pattern Recognition, the premier conference of its kind, will feature two papers co-authored by an LLNL researcher targeted at improving the understanding of robust machine learning models.

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The ADAPD program held a two-day virtual meeting to highlight science-based and data-driven analysis work to advance AI innovation and develop AI-enabled systems to enhance the U.S. capability to detect nuclear proliferation activities around the globe.

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New research debuting at ICLR 2021 demonstrates a learning-by-compressing approach to deep learning that outperforms traditional methods without sacrificing accuracy.

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LLNL is looking for participants and attendees from industry, research institutions and academia for the first-ever Machine Learning for Industry Forum (ML4I), a three-day virtual event starting Aug. 10. The event is sponsored by LLNL’s High Performance Computing Innovation Center and the Data Science Institute.

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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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