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. I co-organized (with Ziwei Zhu) the Statistics & Machine Learning in the Big Data Era 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