Deanna Needell, professor of mathematics and the Dunn Family Professor of Data Theory, has been selected as a 2024 SIAM Fellow — one of the Society for Industrial and Applied Mathematics’ highest honors — in recognition of her contributions to compressed sensing, stochastic optimization and applied data science.

Big data is now a ubiquitous phrase, but its meaning is true to form with data literally everywhere and growing exponentially. From tech companies that employ colossal teams of data scientists to process and understand their data to the smallest nonprofit seeking to understand how to better serve its constituents, data can be critical to success.

Efficient, robust and practical methods for data analysis are needed to handle and understand the recent surge of applications involving large-scale data. This requires mathematical tools ranging from dimension-reducing, geometry-preserving maps that can be applied and stored efficiently, to methods for interpretable factorization, and more fair and equitable optimization methods for both high-level and subroutine problems.

Needell’s research spans this range, advancing both scientific understanding of mathematical objects relating to large-scale data science as well as the development of methods that can be used in practical settings.