Deep Learning has revolutionized the fields of computer vision, speech recognition and control systems. Can Deep Learning work for scientific problems? This talk will explore a variety of DOE/LBL applications that are currently benefiting from Deep Learning. We will review classification and regression problems in astronomy, cosmology, neuroscience, genomics and high-energy physics. We will outline several short and long-term challenges at the frontier of DL research, and speculate about the role of DL and AI in the future of scientific discovery.
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 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’.
Outside attendees need to contact Barry Schneider in order to obtain the site badges required to enter NIST grounds and to attend the seminar. 24 hour notice is required for US citizens and 3 days for non-US citizens. Please contact firstname.lastname@example.org to be added to the visitor list. Visitors must check in at the NIST Visitor Center to pick up their badges. A photo ID is required for US citizens and a passport or green card for foreign nationals.