Thomas Lee

Thomas Lee, Ph.D., is a Professor of Statistics at the University of California, Davis, affiliated with the Department of Statistics within the College of Letters and Science. His research focuses on nonparametric and semiparametric modeling, statistical learning, signal processing, and applications across scientific disciplines. He holds a Ph.D. from Macquarie University and CSIRO, Australia. Recent work emphasizes time series analysis, survey methodology, and computational statistics. His publications span journals like Signal Processing, Journal of Survey Statistics and Methodology, and Journal of the Royal Statistical Society Series B.

Research interests include developing statistical methodologies for complex data structures, such as spatial and spherical data, and addressing challenges in model calibration and robust matrix completion. His work integrates frequentist approaches with modern computational techniques to solve real-world problems in fields like geophysics and environmental science.

Publications highlight advancements in statistical signal processing for count data, survey regression with Lasso regularization, and multi-resolution modeling of non-Gaussian random fields. While no awards are explicitly listed, his prolific output reflects significant contributions to statistical theory and applications.