My research focuses on largescale data analysis and optimization In particular, I design, analyze, and implement optimization algorithms for largescale (data intensive) problems in statistics, machine learning, and computational science. My main mathematical tools draw upon: theoretical computer science, statistics, signal processing, matrix analysis, harmonic analysis, convex and nonconvex optimization, stochastic programming, and numerical linear algebra.
More broadly, I am usually interested in all things computational, where one can not only implement algorithms, but also prove some theorems!

Primary Projects and Research Areas
 Machine learning, data mining, computational statistics
 Inverse Problems in Signal Processing, Medical Imaging, Astronomy, Computer Vision
 Inexact Computation: Optimization under error and uncertainty
 Largescale (continuous) optimization:
 Largescale linear and quadratic programming
 Distributed and Parallel Nonlinear Optimization
 Largescale nonsmooth convex and nonconvex optimization
 Search and retrieval in databases of structured objects (images, proteins, etc.)
 Matrix Analysis: matrix means, positive definite matrices, kernel function theory, etc.
