Contact Information:
School of Statistics University of Minnesota ktan @ umn.edu |
I am currently an assistant professor in the School of Statistics at University of Minnesota. Previously, I was a postdoctoral research associate supervised by Professors Han Liu and Tong Zhang. Prior to this, I obtained my PhD degree in the Department of Biostatistics at University of Washington, under the supervision of Dr. Daniela Witten.
I am interested in developing statistical machine learning methods for analyzing high-dimensional data sets arising from genomics, neuroscience, and other areas of biology. More specifically, I develop unsupervised learning methods such as probabilistic graphical models, cluster analysis, and dimension reduction to uncover patterns from massive data set. |
Selected Papers:
Propagation of Information along the Cortical Hierarchy as a Function of Attention while
Reading and Listening to Stories [bioRxiv]
Regev M, Simony E, Lee K, Tan KM, Chen J, and Hasson U (2018)
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Tan KM, Wang Z, Liu H and Zhang T. (2018)
Journal of the Royal Statistical Society: Series B, in press
A Convex Formulation For High-Dimensional Sparse Sliced Inverse Regression
Tan KM, Wang Z, Zhang T, Liu H and Cook R.D. (2018)
Biometrika, in press
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation [link]
Sun Q, Tan KM, Liu H, and Zhang T (2018)
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4817-4824, 2018 `
Replicates in High Dimensions, with Applications to Latent Variable Graphical Models [link]
Tan KM, Ning Y, Witten D and Liu H (2016)
Biometrika, 103(4):761-777
Propagation of Information along the Cortical Hierarchy as a Function of Attention while
Reading and Listening to Stories [bioRxiv]
Regev M, Simony E, Lee K, Tan KM, Chen J, and Hasson U (2018)
Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow
Tan KM, Wang Z, Liu H and Zhang T. (2018)
Journal of the Royal Statistical Society: Series B, in press
A Convex Formulation For High-Dimensional Sparse Sliced Inverse Regression
Tan KM, Wang Z, Zhang T, Liu H and Cook R.D. (2018)
Biometrika, in press
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation [link]
Sun Q, Tan KM, Liu H, and Zhang T (2018)
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4817-4824, 2018 `
Replicates in High Dimensions, with Applications to Latent Variable Graphical Models [link]
Tan KM, Ning Y, Witten D and Liu H (2016)
Biometrika, 103(4):761-777