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Podcast: Deep Learning for Scientific Data Analysis

Debbie Bard from NERSC

In this NERSC News Podcast, Debbie Bard from NERSC describes how Deep Learning can help scientists accelerate their research.

“Deep learning is enjoying unprecedented success in a variety of commercial applications, but it is also beginning to find its footing in science. Just a decade ago, few practitioners could have predicted that deep learning-powered systems would surpass human-level performance in computer vision and speech recognition tasks.”

These tools are now poised to help scientists contend with some of the most challenging data analytics problems in a number of domains. For example, extreme weather events pose great potential risk on ecosystem, infrastructure and human health. Analyzing extreme weather data from satellites and weather stations and characterizing changes in extremes in simulations is an important task. Similarly, upcoming astronomical sky surveys will obtain measurements of tens of billions of galaxies, enabling precision measurements of the parameters that describe the nature of dark energy. But in each case, analyzing the mountains of resulting data poses a daunting challenge.

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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