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Oscar Madrid Padilla is an Assistant Professor in the Department of Statistics at the University of California, Los Angeles (UCLA). His research interests center around high dimensional statistics, network estimation problems, change point detection, Bayesian statistics, quantile regression, and graphical models. He has published several papers addressing these topics and aims to push the boundaries of statistical methods in modern data analysis. His work often involves leveraging Bayesian techniques to analyze and interpret complex data structures. Padilla is also dedicated to teaching and mentoring students, sharing his expertise in statistical methods and data science.
Department of Economics admits primarily for the PhD program.