Contact Information:
445C West Hall Department of Statistics University of Michigan keanming @ umich.edu |
I am currently an assistant professor at the Department of Statistics at University of Michigan. Previously, I was an assistant professor at the School of Statistics at University of Minnesota, and a postdoctoral research associate supervised by Han Liu and Tong Zhang. I joined the University of Washington in 2011 for my PhD degree, under the supervision of Daniela Witten.
I am an applied statistician working on statistical machine learning methods for analyzing complex biomedical data sets. I develop multivariate statistical methods such as probabilistic graphical models, cluster analysis, discriminant analysis, and dimension reduction to uncover patterns from massive data set. I also work on topics related to robust statistics, quantile regression, non-convex optimization, and data integration from multiple sources. More recently, I am involved in applying instrumental variable to models with unmeasured confounders. I am looking for one motivated PhD student this year to explore some of the aforementioned topics. Send me an email if you are interested in learning more. |
Former and Current Students
Recent Papers: [Google scholar]
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization [arXiv] [R implementation] [python implementation]
Tan KM, Wang L and Zhou W-X (2021+)
Journal of the Royal Statistical Society: Series B, in press
Smoothed Quantile Regression with Large-Scale Inference [preprint] [R package conquer]
He X, Pan X, Tan KM and Zhou W-X (2021+)
Journal of Econometrics, in press
Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks [arXiv] [link]
Tan KM, Lu J, Zhang T and Liu. H (2021)
Biometrics, 77(2):379--390.
Transformation of Speech Sequences in Human Sensorimotor Circuits [link]
Musch K, Himberger K, Tan KM, Valiante TA and Honey CJ (2020)
Proceedings of the National Academy of Sciences, 117(6):3203--3213.
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow [link] [arXiv] [R package rifle]
Tan KM, Wang Z, Liu H and Zhang T (2018)
Journal of the Royal Statistical Society: Series B, 80(5):1057-1086
Recent Papers: [Google scholar]
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization [arXiv] [R implementation] [python implementation]
Tan KM, Wang L and Zhou W-X (2021+)
Journal of the Royal Statistical Society: Series B, in press
Smoothed Quantile Regression with Large-Scale Inference [preprint] [R package conquer]
He X, Pan X, Tan KM and Zhou W-X (2021+)
Journal of Econometrics, in press
Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks [arXiv] [link]
Tan KM, Lu J, Zhang T and Liu. H (2021)
Biometrics, 77(2):379--390.
Transformation of Speech Sequences in Human Sensorimotor Circuits [link]
Musch K, Himberger K, Tan KM, Valiante TA and Honey CJ (2020)
Proceedings of the National Academy of Sciences, 117(6):3203--3213.
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow [link] [arXiv] [R package rifle]
Tan KM, Wang Z, Liu H and Zhang T (2018)
Journal of the Royal Statistical Society: Series B, 80(5):1057-1086