Jean-Eudes Dazard, Ph.D.

Faculty Appointments Center for Proteomics and Bioinformatics: Assistant Professor Biostatistics and Bioinformatics Core Facility of the Comprehensive Cancer Center: Biostatistician Bioinformatics and Biostatistics Core of the Center for Metabolomics and Isotopomics: Director

Academic History

Positions and Employment

Research Interests

Conventional statistical techniques and methods literally fall apart or are inappropriate at best when dealing with modern large datasets where the number of variables greatly exceeds the number of observations (so-called p >> n problem). It is a hard problem with several statistical issues causing potential risks of severe errors and model unfitting. Particular challenges posed by high dimensional data are the multiplicity of inferences and the control of error rates, the multi-collinearity of predictors due to the parallel nature of the variables, and finally the sparsity due to inherent noise from the employed technologies and the fewness of variables at play compared to the massive number of variables interrogated.

My research interest is in computational/statistical biology with emphasis on developing data mining methods in high dimensional settings (p >> n paradigm). Applications are in high-throughput or "omics" data as generated by microarray, proteomics and high-throughput sequencing technologies. My focus is in:

1. Bump Hunting problems and applications to diagnostic and prognostic data mining tools in an effort to design individual and tailored treatments to patients, a step towards personalized medicine.
2. Bayesian and Frequentist Model Selection and Predictive Modeling (Classification, Regression) as applied to Differential Expression and Genetic Interaction problems.
3. Regularization and Variance Stabilization of high-dimensional data. Statistical Computing: Monte-Carlo methods. Parallel computing. Computational complexity (algorithmic and memory) of large datasets

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My Publications