Ricardo Masini

Ricardo Masini is an Assistant Professor in the Department of Statistics at the University of California, Davis, where he conducts research at the intersection of statistical theory, econometrics, and data science. His work bridges theoretical developments with practical applications in complex data environments.

Research Interests: Ricardo's research focuses on foundational aspects of statistical inference under dependence and high dimensionality. Key areas include high-dimensional central limit theorems, strong approximation techniques for dependent data, factor models and their connections to sparse modeling, causal inference in settings with a single or few treated units, and higher-order refinements for U-statistics using Edgeworth expansions.

His recent publications accepted in the Annals of Statistics and the Journal of Econometrics reflect a strong trend toward improving the accuracy and applicability of asymptotic methods in non-standard and high-dimensional settings, particularly through coupling and higher-order expansion techniques.

Funding:

• NSF DMS-2210561 – Supporting research in statistical theory and dependent data.

He advises students in statistics and econometrics and is actively contributing to advancing methodological tools for modern data analysis. His work is collaborative, often involving leading scholars in the field such as Matias Cattaneo, Max Farrell, and Michael Jansson.