معرفی
Miles Lopes is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on developing and analyzing bootstrap methods for high-dimensional statistical problems, with particular attention to error estimation in randomized algorithms and high-dimensional inference. He holds a Ph.D. from UC Berkeley.
Research interests include bootstrap approximation techniques, high-dimensional covariance estimation, spectral statistics, and applications of randomized algorithms in numerical linear algebra. His work bridges theoretical statistics and computational methods, addressing challenges in modern data analysis.
Recent publications emphasize bootstrap methods for eigenvalue analysis in high-dimensional PCA, operator norm approximation, and robust statistical inference in complex models. His methodologies have applications in functional data analysis, multivariate testing, and software development for randomized algorithms.
