My PhD thesis, defended in May 2013 at the California Institute of Technology under the advisement of Professor Joel Tropp. randomized matrix sparsification algorithms, low-rank approximation ...

PARLA is a Python package for prototyping the mathematical structure of a future C++ library for randomized numerical linear algebra. The future library is meant to be "LAPACK-like" and will organize ...

Randomized algorithms for very large matrix problems have received a great deal of attention in recent years. Much of this work was motivated by problems in large-scale data analysis, largely since ...

Randomized Numerical Linear Algebra (RandNLA) is an interdisciplinary research area that exploits randomization as a computational resource to develop improved algorithms for large-scale linear ...

Randomized algorithms are algorithms that use random numbers or choices to achieve their goals, such as solving a system of linear equations, computing a matrix inverse, or finding a low-rank ...

Randomized algorithms have become an essential tool in solving linear systems and least squares problems, particularly in large-scale applications. These algorithms leverage randomness to improve ...

His research revolves around developing fast and efficient randomized algorithms for various large-scale statistical, as well as more general linear algebraic problems. Using the tools from randomized ...

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly ...

Randomized algorithms for very large matrix problems have received a great deal of attention in recent years. Much of this work was motivated by problems in large-scale data analysis, largely since ...