Session Abstract – PMWC 2019 Silicon Valley
Session Synopsis: We see an emerging era of potential explosive growth of AI applications in healthcare, but the implementation and access to AI is challenged by isolated data stores and the high degree of complexity of the available tools. Furthermore, deep learning requires lots of data to train and refine models which, in turn, complicates and burdens compute systems as well as data movement. This panel will discuss AI and machine learning systems requirements, and potential paths to overcoming the challenges of AI implementation.
Session Chair Profile
B. Tech, Sc.M, 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 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’.