Relaxing Bottlenecks for Fast Machine Learning

Date(s):

Location:
Jacobs School of Engineering, 9500 Gilman Dr, La Jolla, San Diego, California 92093

Sponsored By:
Stefanie Battaglia

Speaker(s):
CHRISTOPHER DE SA
Christopher De Sa

Speaker Bio:
Christopher De Sa is a PhD candidate in Electrical Engineering at Stanford University advised by Christopher RĂ© and Kunle Olukotun. His research interests include algorithmic, software, and hardware techniques for high-performance machine learning, with a focus on relaxed-consistency variants of stochastic algorithms such as asynchronous stochastic gradient descent (SGD). He is also interested in using these techniques to construct data analytics and machine learning frameworks that are efficient, parallel, and distributed. Chris's work on studying the behavior of asynchronous Gibbs sampling received the Best Paper Award at ICML 2016.

Contact:
Stefanie Battaglia
Executive Assistant to the Department Chair
sbattaglia@ucsd.edu | Ph: (858) 534-7013