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Andrew Barron is a Professor in the Applied Mathematics Program at Yale University, specializing in Statistics and Data Science. He serves as the Co-Director of Graduate Studies. His research interests encompass statistical information theory, probability limit theorems, function estimation, and neural networks. With a strong focus on the theoretical foundations of statistics, Barron has contributed significantly to understanding various aspects of statistical learning and information theory, making important advancements in the realms of estimation and neural network applications. He is actively involved in guiding graduate studies and contributing to the academic community at Yale University.
Administered via the Graduate School of Arts and Sciences (GSAS). GRE General is optional for PhD.