Deep Learning at Scale for Cosmology Research

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Debbie Bard from NERSC

In this video from Google I/O 2018, Debbie Bard from NERSC describes Deep Learning at scale for cosmology research.

“Analyzing the data collected by telescope surveys of the sky is in itself a big data/HPC problem. Current surveys (such as the Dark Energy Survey) collect measurements of tens of millions of galaxies and, in the next 10 years, astronomers will collect data on tens of billions of galaxies. The data storage and analysis needs here are complex — astronomical data is noisy and can take many forms.”

Debbie Bard is acting group lead for the Data Science Engagement Group at the National Energy Research Scientific Computing Center (NERSC) at Berkeley National Lab. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her Ph.D. at Edinburgh University, and worked at Imperial College London and SLAC National Accelerator Laboratory before joining the Data and Analytics group at NERSC, where she focuses on data-intensive computing and research.

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