A formal framework for knowledge acquisition: Going beyond machine learning

O. Hossjer, D.A. Diaz-Pachon and J.S. Rao

J. Sunil Rao's interests are in applied statistics, biostatistics, small area estimation and machine learning. His current research focuses on cancer, health disparities and opioid relapse. He has also developed software packages with various students and collaborators.

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Research Interests:

High throughput cancer genomic data modeling;
Health disparity estimation;
Opioid relapse prediction;
Machine learning;
High dimensional modeling; Bayesian model selection; Mixed model selection
and prediction;
Small area estimation; Bump/mode hunting;
Robust estimation;
Precision medicine;
Modeling of
pharmacogenomic data


Software Sites


A statistical technique for detecting differentially expressing genes from microarray data using Bayesian ANOVA

Fence Methods

A class of strategies for selection of fixed and random factors in linear and generalized linear mixed models
MOst Recent

Publications and Articles


Predicting cervical cancer DNA methylation from genetic data using multivariate classified mixed model prediction

H. Zhang, J.S. Rao, S. Chen, R. Balise and N. Ayad

Correcting prevalence estimation from biased samples with testing errors

L. Zhou, D. Diaz-Pachon, Z. Chen and J.S. Rao

Mode Hunting using Pettiest Components Analysis

T. Liu, D. Diaz, J.S. Rao and J.E. Dazard
(to appear in IEEE Transactions On Pattern Analysis and Machine Intelligence)
J. Sunil Rao, Ph.D.

Professor and Director, Division of Biostatistics
Miller School of Medicine, University of Miami

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