Deep Learning for Science: Capabilities and Challenges for Transforming Scientific Workflows

Date and Time: 
KEYNOTE - Tuesday, April 9th 2019
CG auditorium
Steve Farrell

Modern science is getting bigger in every way: the scope of the problems, the size of the datasets, and the required computing power. Massive undertakings such as the Large Hadron Collider and the Large Synoptic Survey Telescope as well as major looming challenges such as global climate change are pushing science past its limits. Deep Learning, a methodology that has seen tremendous success in industry applications, is poised to transform scientific workflows. Scientists are increasingly utilizing the expressive power of deep neural networks to learn complex data representations and distributions to get more out of their data and replace expensive or tedious analytics tasks. Scientists are also increasingly utilizing large scale computing resources such as supercomputers to train and deploy their deep learning models. In this presentation I will describe some of the key technologies enabling Deep Learning solutions for science as well as some of the open challenges that stand as potential roadblocks using examples from high energy physics, cosmology, and climate science.

Speaker Description: 

Steve Farrell is a Machine Learning Engineer at the NERSC supercomputing center at Lawrence Berkeley National Laboratory. In this role he supports the ML needs of 7000 users across a wide range of scientific domains, and the ML software stack. He collaborates with science teams to perform applied ML research.

Video recorded: 

The slides are avaialble here

Event Category: