Tom Goldstein
 
 

I am a post-doctoral fellow in Rich Baraniuk’s  Digital Signal Processing group. I obtained my PhD in Applied Mathematics at UCLA in 2010, and completed a post-doctoral fellowship at Stanford University in 2012. 

    My research interests lie in large scale optimization and distributed algorithms for “big data.” My work has applications in machine learning and image processing, especially for magnetic resonance imaging (MRI) and computed tomography (CT).  A common thread in my research is the development of fast algorithms for large scale problems - problems for which conventional “off the shelf” techniques are not computationally tractable. 

    My research on optimization methods for image processing and computer vision has recently been featured by Thomson Reuters Sciencewatch as a “New Hot Paper in Computer Science.”  

 

Biography

Postdoctoral Fellow

Rice University

Electrical & Computer Engineering

Curriculum Vitae (Dec. 2013): pdf 


Software tools I have developed:

Perfusion Imaging Toolkit

CGIST

Split Bregman Methods