Research: Parallel Computing for Machine Learning
Abstract
The goal of this project is to discover, understand, and exploit the available parallelism in machine learning algorithms as applied to big data problems.
Intellectual Merit
explore parallel computing for machine learning by working on real problems and designing parallel machine learning algorithms
Broader Impact
This project will provide invaluable research, educational and training opportunities to students at both undergraduate and graduate levels.
Use of FutureGrid
We will use FutureGrid resources as a testbed.
Scale Of Use
About 10 research collaborators.
Publications
FG-340
Wilson Rivera
University of Puerto Rico
Active
Project Members
Andres Malines
Carlos Gomez
Christian Montes
Henry Estepar
Jorge Perea
Juan Nieves
Omar Soto
Oscar Gomez
Rogelio Vázquez
FutureGrid Experts
Saliya Ekanayake