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]
Educational Disparities in STEM during COVID-Induced Distance Learning and a Potential Strategy
to Address Them
Man R, Li J and Tan KM (2026)
Nature Communications, in press.
Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS [link]
Yu M, Wang Y, Xie S, Tan KM and Zhou W-X (2025)
Journal of the American Statistical Association, in press.
Robust Estimation and Inference for Expected Shortfall Regression with Many Regressors [arXiv][link]
He X, Tan KM and Zhou W-X (2023)
Journal of the Royal Statistical Society: Series B, 85(4), 1223--1246.
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
Recent Papers: [Google scholar]
Educational Disparities in STEM during COVID-Induced Distance Learning and a Potential Strategy
to Address Them
Man R, Li J and Tan KM (2026)
Nature Communications, in press.
Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS [link]
Yu M, Wang Y, Xie S, Tan KM and Zhou W-X (2025)
Journal of the American Statistical Association, in press.
Robust Estimation and Inference for Expected Shortfall Regression with Many Regressors [arXiv][link]
He X, Tan KM and Zhou W-X (2023)
Journal of the Royal Statistical Society: Series B, 85(4), 1223--1246.
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