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

Keywords

big data, machine learning, parallel computing