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 from Princeton University and Tong Zhang from Rutgers University. 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 PapersSparse 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 |
PresentationsPoster on Dynamic Reconfiguration of Brain Network under Natural Continuous Stimulus
Slides on Replicates in High Dimensions, With Application to Latent Variable Graphical Models
Slides on Statistical Properties of Convex Clustering Poster on Learning Graphical Models with Hubs
|
