Kean Ming Tan, PhD
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 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 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 (2022+)
Journal of the Royal Statistical Society: Series B, 84(1): 205--233.

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

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

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