Contact Information:
445C West Hall Department of Statistics University of Michigan keanming @ umich.edu |
I am currently an associate professor at the Department of Statistics at University of Michigan.
I am a statistician working on developing methods for analyzing complex/heterogeneous large scale data sets arising in modern scientific applications. A central goal of my research is to develop flexible and robust methods with practical algorithms that can be reliably applied to large scale data sets. Our work integrates ideas from statistical learning, optimization, and probability theory. One of our current focuses is on distribution-sensitive methods that go beyond mean-based analysis such as quantiles and expected shortfalls that characterize the tail behavior of a distribution. Please do not hesitate to contact me if you have complex and heterogeneous data, we are always interested in looking into new datasets! I am currently serving on the editorial boards for the Journal of the Royal Statistical Society: Series B, Journal of the American Statistical Association, and Statistica Sinica. 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! Due to ongoing commitments with current students, I am unable to take on new students for research at this time. |
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