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Upcoming Seminar

May 18: Katiana Kontolati (Johns Hopkins University). Contact datascience [at] llnl.gov (subject: DSI%20seminar%20series) for a WebEx invitation.

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Understanding the Universe with Applied Statistics (3:57), featuring Dr. Amanda Muyskens.

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Data Scientist Spotlight

Rebecca Rodd

Rebecca Rodd

Data Specialist

A crucial aspect of data science—particularly at LLNL and across the Department of Energy (DOE)—is the management of big data across different domain expertise and programs. Since joining the Lab’s geophysical monitoring programs in 2019, Rebecca Rodd has focused on data cleaning and ingestion, developing geophysical data management standards, and building data infrastructure for several of NNSA’s Defense Nuclear Nonproliferation R&D projects. “Over the last decade, innovation in data management techniques and tools has led to improvements in data storage, rapid data transfer, integrated data management systems, cloud and hybrid computing, and other areas,” she explains. “The DOE has many data management successes, and applying them to geophysical programs and datasets is exciting and challenging due to the multi-laboratory and multi-phenomenology requirements, solving multi-lab access control and varied security policies, and inconsistent metadata standards across and within domain areas.” Rodd thrives on learning new technologies and in 2022 assumed leadership of the annual DOE Data Days workshop (D3), which brings together data management practitioners, researchers, and project managers to promote data management for higher quality, more efficient R&D across the DOE complex. She notes, “Data management often does not get as much attention at data-focused DOE meetings, so D3 offers a place for more collaboration in this area.” Rodd holds a B.S. in Geology from UC Davis and an M.S. in Geosciences from the University of North Carolina at Chapel Hill.

New Research in AI

SambaNova AI Hardware to Support CogSim Research

Brian Van Essen and Bronis de Supinski stand next to the SambaNova hardware

LLNL and SambaNova Systems have announced the addition of a spatial data flow accelerator into the Livermore Computing Center, part of an effort to upgrade the Lab’s CogSim program. LLNL will integrate the new hardware to further investigate CogSim approaches combining AI with high-performance computing—and how deep neural network hardware architectures can accelerate traditional physics-based simulations as part of the National Nuclear Security Administration’s Advanced Simulation and Computing program. The Lab is expected to use the SambaNova AI systems to improve the fidelity of models and manage the growing volumes of data to improve overall speed, performance and productivity for stockpile stewardship applications, fusion energy research and other basic science work. “Multiphysics simulation is complex,” says Informatics Group Leader Brian Van Essen (pictured at left with LLNL CTO Bronis de Supinski). “Our inertial confinement fusion experiments generate huge volumes of data. Yet, connecting the underlying physics to the experimental data is an extremely difficult scientific challenge. AI techniques hold the key to teaching existing models to better mirror experimental models and to create an improved feedback loop between the experiments and models. The SambaNova system will help us create these cognitive simulations.” Read more at LLNL News.

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