Cynthia Rush

Cynthia Rush is an Associate Professor in the Department of Statistics at Columbia University, where she conducts research at the intersection of mathematical statistics, information theory, and high-dimensional data analysis. She is affiliated with the Graduate School of Arts and Sciences and is based in the School of Social Work building in New York City.

• Ph.D. in Statistics, Yale University, 2016
• B.S. in Mathematics, University of North Carolina at Chapel Hill

Her research focuses on the development and analysis of algorithms for high-dimensional inference, particularly using Approximate Message Passing (AMP), with applications in sparse regression, signal processing, and machine learning. She integrates tools from information theory, statistical physics, and applied probability to understand fundamental limits and performance of modern statistical methods.

Her recent publications span topics such as SLOPE regularization, community detection, power posteriors, and massive multiple access in communications. These works appear in top journals including IEEE Transactions on Information Theory, Annals of Statistics, and NeurIPS. Her research shows a consistent trend toward rigorous theoretical analysis of high-dimensional statistical algorithms and their practical implications.

Notable scientific recognition includes being a finalist for the JKW Student Paper Award at IEEE ISIT in 2016. She has delivered numerous invited talks at leading institutions and conferences worldwide, reflecting her active role in the theoretical data science community.

• JKW Student Paper Award Finalist, IEEE ISIT 2016

She advises and collaborates with researchers in statistics, machine learning, and information theory, though formal doctoral advisees are not listed. Her work is supported by theoretical grants and collaborations across engineering and mathematical sciences. She is involved in the broader academic community through seminars, workshops, and program committees, contributing to the advancement of rigorous statistical learning.

Cynthia Rush leads a research group focused on high-dimensional inference and algorithmic analysis, often collaborating with researchers at institutions like Yale, Princeton, and international centers in Europe and Asia. Her lab emphasizes theoretical foundations with practical relevance in data science and communications.