Presentation: Semi-Supervised Deep Learning for Climate @ Scale

Track: Machine Learning 2.0

Location: Majestic Complex, 6th fl

Duration: 10:35am - 11:25am

Day of week: Wednesday

Level: Advanced

Persona: Data Scientist


Climate change is one of the most important problems facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to understand the evolution of the climate system subject to various CO2 emission scenarios. Large scale climate simulations produce 100TB-sized spatio-temporal, multi-variate datasets, making it difficult to conduct sophisticated analytics. In this talk, I will present our results in applying Deep Learning for supervised and semi-supervised learning of extreme weather patterns. I will briefly highlight our efforts in scaling Deep Learning to 9000 KNL nodes on a supercomputer. The audience will gain a better appreciation for the breadth of problems for which Deep Learning has been successfully applied, and insights into open challenges for this emerging field.

Speaker: Prabhat

Data and Analytics Group Lead @NERSC

Prabhat leads the Data and Analytics Services team at NERSC. His current research interests applied statistics, machine learning, and high performance computing. He has worked on topics in scientific data management, parallel I/O, scientific visualization, computer graphics and computer vision in the past. 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.

Find Prabhat at


Monday, 26 June

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