KarlRohe
Broad Professional Interests
KNOWLEDGE CREATION WITH STATISTICS
data collection, exploratory analysis, visualization, modeling, validation.
STATISTICAL MACHINE LEARNING
special attention to multivariate methods, clustering, and computationally tractable methods for model selection in
high-dimensions.
APPLICATION TO COMPLEX SYSTEMS
e.g. social networks
J. Jia and K. Rohe.** “Preconditioning to comply with the Irrepresentable Condition.” [pdf]
K. Rohe, T. Qin, H. Fan. “The Highest Dimensional Stochastic Blockmodel with a Regularized Estimator.” [pdf]
K. Rohe and B. Yu. “Co-clustering for directed graphs; the stochastic co-blockmodel and a spectral algorithm.” Technical Report. [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.” In press at Statistica Sinica. [pdf]
** Authors contributed equally.
Publications
August 4-8. I will speak at JSM in Montreal.
July 23-25. I will attend the Duke Workshop on Sensing and Analysis of High-Dimensional Data.
April 5. I will speak at NYU Stern’s IOMS seminar series.
Feb 22. I will speak at the Duke ECE department on preconditioning.
January 30 - Feb 1. Great workshop at Eurandom.
November 14th and 15th 2012. I will be at Johns Hopkins University. I will speak in the Applied Math and Stat Dept seminar on Thursday the 15th.
August 2012: A simple preprocessing step can make the Lasso sign consistent under less restrictive assumptions. See the details in this paper with J Jia.
Updates
(let me know if you will be nearby and would like to meet.)
photo courtesy of Frances Tong
Any comments, reviews, critiques, or objections are invited and should be sent to me by e-mail. If co-authors, you, and I agree, then I will provide a link to our discussion below.