Deep Learning for Science

Special guest seminar hosted by Physics & the Data Science Initiative by Mr. Prabhat

Abstract: Deep Learning has revolutionized the fields of computer vision, speech recognition and robotics in recent years. Can Deep Learning work for science? This talk will review results from the application of Deep Learning to problems in climate, astronomy, cosmology, neuroscience, genomics and high-energy physics. We will review the landscape of data analytics problems in science, comment on the applicability of Deep Learning to a subset of problems, and call out open challenges for the future. The talk will conclude with a speculation about the future of AI in scientific discovery.

About: Prabhat leads the Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization. He is also interested in applied statistics, machine learning, computer graphics and computer vision. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.

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