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 a statistician working on statistical machine learning methods for analyzing complex 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. We organize the Modern Statistical and Machine Learning Methods for Big Data Workshop that takes place in Michigan Ann Arbor at October 21-22, 2022. Check out the details here! |
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 (2022)
Journal of the Royal Statistical Society: Series B, 84(1): 205--233.
Scalable Estimation and Inference for Censored Quantile Regression Process [preprint]
He X, Pan X, Tan KM and Zhou W-X (2022+)
The Annals of Statistics, in press
Smoothed Quantile Regression with Large-Scale Inference [preprint] [R package conquer]
He X, Pan X, Tan KM and Zhou W-X (2022+)
Journal of Econometrics, in press
Sparse Reduced Rank Huber Regression in High Dimensions [link] [code]
Tan KM, Sun Q and Witten D (2022+)
Journal of the American Statistical Association, in press
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 (2022)
Journal of the Royal Statistical Society: Series B, 84(1): 205--233.
Scalable Estimation and Inference for Censored Quantile Regression Process [preprint]
He X, Pan X, Tan KM and Zhou W-X (2022+)
The Annals of Statistics, in press
Smoothed Quantile Regression with Large-Scale Inference [preprint] [R package conquer]
He X, Pan X, Tan KM and Zhou W-X (2022+)
Journal of Econometrics, in press
Sparse Reduced Rank Huber Regression in High Dimensions [link] [code]
Tan KM, Sun Q and Witten D (2022+)
Journal of the American Statistical Association, in press
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