Associate Professor of Statistics, courtesy appointment in Electrical Engineering, University of Wisconsin--Madison.

ML @ Madison!

Editorial Service: AE at JRSS-B and JNPS.


graph sampling, community detection, graph contextualization, twitter.

particularly multivariate methods and clustering.


K Rohe.  “Network driven sampling; a critical threshold for design effects.” Accepted at Annals of Statistics. [pdf] [code]

Karl Rohe, Jun Tao, Xintian Han, Norbert Binkiewicz. "A note on quickly sampling a sparse matrix with low rank expectation" [pdf] [code]

Sebastien Roch, Karl Rohe. "Generalized least squares can overcome the critical threshold in respondent-driven sampling" [pdf] [code]

Yilin Zhang, Marie Poux-Berthe, Chris Wells, Karolina Koc-Michalska, Karl Rohe.  "Discovering Political Topics in Facebook Discussion threads with Graph Contextualization" [pdf] [code]

J Cho, D Kim, and K Rohe.  “Intelligent initialization and adaptive thresholding for iterative matrix completion; some statistical and algorithmic theory for adaptive-impute.” Accepted at Journal of Computational and Graphical Statistics. [pdf]

X Li and K Rohe.  “Central limit theorems for network driven sampling.” Electronic Journal of Statistics. [pdf]

M Khabbazian, B Hanlon, Z Russek, and K Rohe.  “Novel sampling design for respondent-driven sampling.” Electronic Journal of Statistics. [pdf]

M Khabbazian, R Kriebel, K Rohe, and C Ané.  “Fast and accurate detection of evolutionary shifts in ornstein-uhlenbeck models.” Methods in Ecology and Evolution, 7(7):811–824, 2016.

T Le, D Bolt, E Camburn, P Goff, and K Rohe.  “Latent factors in student-teacher interaction factor analysis.” Journal of Educational and Behavioral Statistics (an ASA journal).

J Cho, D Kim, and K Rohe.  “Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise.” Statistica Sinica. [pdf]

K Rohe, T Qin, and B Yu.  “Co-clustering directed graphs to discover asymmetries and directional communities.” Proceedings of the National Academy of Sciences. [tech report] [code]

N Binkiewicz, JT Vogelstein, K Rohe.  “Covariate Assisted Spectral Clustering.” Biometrika.  [pdf]

K Rohe.  “Preconditioning for classical relationships: a note relating ridge re- gression and ols p-values to preconditioned sparse penalized regression.”  Stat (ISI journal for rapid publication), 4(1):157–166, 2015. [pdf]

V Vu, J Cho, J Lei, K Rohe. “Fantope Projection and Selection: A near-optimal convex relaxation of Sparse PCA”. NIPS 2013.  [pdf]

T Qin and K Rohe.  “Regularized Spectral Clustering Under the Degree-Corrected Stochastic Blockmodel.”  NIPS 2013.  [pdf]

K Rohe and T Qin.  “The Blessing of Transitivity in Sparse and Stochastic Networks.”  [pdf]

J Jia and K Rohe.  “Preconditioning to comply with the Irrepresentable Condition.”  Electronic Journal of Statistics. [pdf]

K Rohe, T Qin, H Fan.  “The Highest Dimensional Stochastic Blockmodel with a Regularized Estimator.” Statistica Sinica. [pdf]

K Rohe, S Chatterjee, and B Yu.  “Spectral clustering and the high-dimensional Stochastic Blockmodel.”  Annals of Statistics, 39(4):1878–1915, 2011. [pdf]

J Jia, K Rohe, and B Yu. “The Lasso under heteroskedasticity.” Statistica Sinica. [pdf]


May 7, 2018.  Statistics colloquium at UChicago.

June 12, 2018.  NetSci Paris.


(let me know if you will be nearby and would like to meet.)

photo courtesy of Frances Tong

This research is supported by NSF grants DMS-1309998, DMS-1612456, and ARO grant W911NF-15-1-0423.