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.
Propagation 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).
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
STAT 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.