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Deep Learning for Science
In recent years, Deep Learning has revolutionized the fields of computer vision, speech recognition and control systems. Can Deep Learning have a positive impact on domain sciences?
The talk will review NERSC’s current and future strategy in Deep Learning. We will review a range of use cases spanning Climate, Cosmology, Astronomy, High-Energy Physics, Neuroscience and Genomics. We will briefly touch upon efforts in scaling Deep Learning on petascale and exascale platforms in DOE. We will conclude with a list of open challenges and speculations about the role of AI in scientific discovery in the future.
Prabhat leads the Data and Analytics Services team at NERSC. His current research interests are in machine learning, statistics and large scale data analytics. He has broad interests in data management, visualization and HPC. 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.
Host: Professor David Laidlaw