Operations Research and Financial Engineering
kmtan @ princeton.edu
I am 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.
I am currently collaborating with neuroscientists at the Princeton Neuroscience Institute to study the reconfiguration of brain connectivity network under natural continuous stimulus. See more details here.
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
Poster 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