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Presentation: Deep Learning for Science

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 4:20pm - 5:10pm

Day of week: Tuesday

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How is deep learning affecting the world of science? In this talk, Prabhat (Data and Analytics team lead at NERSC, Berkeley Lab) discusses machine learning's impact on climatology, astronomy, cosmology, neuroscience, genomics, and high-energy physics. The talk will conclude with a list of open challenges in applied Deep Learning, and the future of AI in powering scientific discoveries.

Speaker: Prabhat

Data and Analytics Group Lead @NERSC

Prabhat leads the Data and Analytics Services team at NERSC; his group is responsible for supporting over 7000 scientific users on NERSC’s HPC systems. His current research interests include Deep Learning, Machine Learning, Applied Statistics and High Performance Computing. In the past, Prabhat has worked on topics in scientific data management; he co-edited a book on ‘High Performance Parallel I/O’. 

Prabhat is the Director of the Big Data Center collaboration between NERSC, Intel, Cray, UC Berkeley, UC Davis, NYU, UBC, Oxford and Liverpool. The BDC project aims at enabling capability, data-intensive science applications on the NERSC Cori system. 

Prabhat received a B.Tech in Computer Science and Engineering from IIT-Delhi (1999) and an ScM in Computer Science from Brown University (2001). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley. Prabhat has co-authored over 150 papers spanning several domain sciences and topics in computer science. He has won 5 Best Paper Awards, 3 Industry Innovation Awards, and he was a part of the team that won the 2018 Gordon Bell Prize for their work on ‘Exascale Deep Learning’.

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.