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. |
Featured PapersPropagation of Information along the Cortical Hierarchy as a Function of Attention while
Reading and Listening to Stories 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 (2016). Under Revision in JRSSB. Replicates in High Dimensions, With Application to Latent Variable Graphical Models Tan KM, Ning Y, Witten D and Liu H. (2016). Biometrika, 103(4):761-777 Statistical Properties of Convex Clustering Tan KM and Witten D. (2015). Electronic Journal of Statistics 9:2324-2347 Learning Graphical Models with Hubs Tan KM, London P, Mohan K, Lee S-I, Fazel M and Witten D. (2014). Journal of Machine Learning Research 15(Oct):3297-3331 Sparse Biclustering of Transposable Data Tan KM and Witten D. (2014). Journal of Computational and Graphical Statistics (23)4:985-1008 |
R Packageshglasso (CRAN link)
An R software package for estimating graphs with hub nodes sparseBC (CRAN link) An R software package for performing sparse biclustering latentGraph (coming soon) An R software package for estimating latent variable graphical models |
TeachingSTAT 5401: We are almost done with Factor Analysis. Next topic we are going to cover will be Cluster Analysis.
STAT 5102: We will move on to the construction of t-test this week. Then we will cover the theoretical aspects of linear models. |
