Kean Ming Tan

Kean Ming Tan is an Associate Professor of Statistics at the University of Michigan, Department of Statistics (College of Literature, Science, and the Arts). His research focuses on statistical machine learning methods for analyzing complex datasets, including multivariate statistical techniques such as probabilistic graphical models, cluster analysis, discriminant analysis, and dimension reduction. He also develops robust statistical methods, quantile regression approaches, non-convex optimization algorithms, and data integration strategies. Recently, his work has involved applying instrumental variables to address unmeasured confounders in models.

Education background and academic training details are not explicitly listed in the provided texts. His research interests span both theoretical and applied domains, with contributions to high-dimensional data analysis, computational statistics, and interdisciplinary applications in neuroscience and econometrics. He co-organized the 2022 'Modern Statistical and Machine Learning Methods for Big Data Workshop' in Ann Arbor, Michigan.

His publications emphasize methodological advancements in quantile regression, robust estimation, and scalable inference techniques. Key themes include handling large-scale datasets, improving model interpretability, and addressing challenges posed by high-dimensionality and latent confounding variables. While specific advising/grant details are not detailed here, his work reflects active engagement in mentoring students and collaborative research initiatives.