Topic: AI/ML

From molecular screening, a software platform, and an online data to the computing systems that power these projects.

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LLNL’s cyber programs work across a broad sponsor space to develop technologies that address the most sophisticated cyber threats directed at disrupting our national security communities and civilian critical infrastructure.

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The Vidya project is a portfolio of research efforts to advance research in physics-informed ML, improve employment of ML with sparse data, invest in validated and explainable ML, explore learning hardware systems in HPC systems, create an advanced ML-tailored data environment, improve simulation workflows, and build ML expertise across the complex.

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LC sited two different AI accelerators at LLNL in 2020: the Cerebras wafer-scale AI engine attached to Lassen; and an AI accelerator from SambaNova Systems into the Corona supercomputing cluster.

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LLNL researchers and a multi-institutional team have developed a highly detailed, machine learning–backed multiscale model revealing the importance of lipids to the signaling dynamics of RAS, a family of proteins whose mutations are linked to numerous cancers.

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LLNL has established the AI Innovation Incubator (AI3), a collaborative hub aimed at uniting experts in artificial intelligence (AI) from LLNL, industry and academia to advance AI for large-scale scientific and commercial applications.

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An LLNL mathematician and collaborators have developed a machine learning–based technique capable of automatically deriving a mathematical model for the motion of binary black holes from raw gravitational wave data.

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LLNL will lend its expertise in vaccine research—most recently from designing new antibodies and antiviral drugs for COVID-19—and computing resources to the Human Vaccines Project consortium to aid development of a universal coronavirus vaccine and improve understanding of immune response.

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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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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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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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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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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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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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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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