Hemant Ishwaran

Professor, Graduate Program Director, Director of Statistical Methodology, Division of Biostatistics, University of Miami

Favorite current quotes:

"Just say no to p-hacking!"
"We are drowning in data but starving for information"
"All models are wrong, but some are useful"

Research Interests

Cancer Staging, Trees, Forests, Ensembles, Bayesian and Frequentist Variable Selection, Bioinformatics, High Dimensions, Nonparametric Bayes

External Activities

Editor, Sankhya A, Sankhya B, 12-15
Deputy Statistical Editor, J. Thoracic and Cardiovascular Surgery
Associate Editor, JASA, Theory and Methods, 05-11
Associate Editor, Electronic Journal of Statistics, 07-13
Associate Editor, Statistics and Probability Letters, 07-10
Web Editor, Inst. of Mathematical Statistics, 03-05

Brief Biography

PhD Statistics, Yale University, 1993
MSc Applied Statistics, Oxford University, 1988
BSc Mathematical Statistics, Univ. of Toronto, 1987

Selected Recent Papers [Full List]

Mantero A. and Ishwaran H. (2017). Unsupervised random forests.

Lu M., Blackstone E.H. and Ishwaran H. (2017). Treatment decision in ischemic cardiomyopathy: causal inference using random survival forests.

Lu M. and Ishwaran H. (2017). A machine learning alternative to p-values. arXiv:1701.04944

Pande A., Li L. Rajeswaran J., Ehrlinger J., Kogalur U.B., Blackstone E.H and Ishwaran H. (2017). Boosted multivariate trees for longitudinal data. Machine Learning, 106(2): 277-305. [pdf]

Lu M., Sadiq S., Feaster D.J. and Ishwaran H. (2017). Estimating individual treatment effect in observational data using random forest methods. To appear in J. Comp. Graph. Statist. arXiv:1701.05306

Tang F. and Ishwaran H. (2017). Random forest missing data algorithms. To appear in Stat. Anal. Data Mining. [pdf]
arXiv:1701.05305

Rice T.W., Kelsen D., Ishwaran H., Blackstone E.H. and Hofstetter W.L. (2016). Eighth Edition of the AJCC Cancer Staging Manual: Esophagus and esophagogastric junction, pp 185-202.

Ishwaran H. (2015). The effect of splitting on random forests. Machine Learning, 99, 75-118. [pdf]

Ishwaran H. and Malley J.D. (2014). Synthetic learning machines. BioData Mining, 7:28. [html] [pdf]

Ishwaran H., Gerds T.A., Kogalur U.B., Moore R.D., Gange S.J. and Lau B.M. (2014). Random survival forests for competing risks. Biostatistics, 15(4), 757-773. doi:10.1093/biostatistics/kxu010 [pdf]

Ehrlinger J. and Ishwaran H. (2012). Characterizing L2Boosting. Ann. Statist, 40, 1074-1101. [pdf]

Chen X. and Ishwaran H. (2012). Random forests for genomic data analysis. Genomics, 99, 323-329. [pdf]

Ishwaran H., Kogalur U.B., Gorodeski E.Z., Minn A.J. and Lauer M.S. (2010). High-dimensional variable selection for survival data. J. Amer. Stat. Assoc, 105, 205-217. [pdf]

Ishwaran H., Blackstone E.H., Hansen. C.A. and Rice T.W. (2009). A novel approach to cancer staging: application to esophageal cancer. Biostatistics, 10, 603-620. [pdf]

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Ann. Appl. Statist., 2, 841-860. [pdf]

randomForestSRC

Random forests R package for survival, regression, and classification. A general implementation of Breiman's random forests.

  • Instructions for installing the OpenMP parallel process package (the CRAN website only has the serial version).

  • New Github repository (coming soon, Spark and Java builds)
    : code and documentation


spikeslab

Spike and slab R package for high-dimensional linear regression models (release 1.1.4). Uses a generalized elastic net for variable selection. Parallel process enabled. pdf


BAMarray (3.0)

Java software for microarray data. Implements Bayesian Analysis of Variance for Microarrays (BAM)

randomSurvivalForest

Random survival forests R package for right-censored and competing risks data (release 3.6.3). pdf