My name is Cynthia Rush and I am an Associate Professor of Statistics in the Department of Statistics at Columbia University. In May, 2016, I received my Ph.D. in Statistics from Yale University under the supervision of Andrew Barron and I completed my undergraduate coursework at the University of North Carolina at Chapel Hill where I obtained a B.S. in Mathematics.
My research uses tools and ideas from information theory, statistical physics, and applied probability as a framework for understanding modern, high-dimensional inference and estimation problems and complex machine learning tasks that are core challenges in the fields of statistics and data science. The thrust of my research is in answering questions like the following. Given a complex statistical problem, how much data, or information, is needed to solve it? How does one most effectively use data to gain insight on the problem at hand and how can we quantitatively describe the limitations of this insight? Can we develop and analyze the performance of computationally-efficient algorithms and procedures for statistical inference and estimation in these settings?