Jean-Eudes Dazard, Ph.D.

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

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 multicollinearity 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 problem). Applications are in high-throughput or "omics" data as generated by microarray, proteomics and sequencing high-throughput technologies. My focus is in:

1. Differential Expression and Predictive Model problems (Classification, Regression)
2. Bump Hunting Problems
3. Bayesian and Frequentist Model Selection
4. Statistical Computing: Resampling techniques. Parallel computing. Computational Issues (complexity and memory) of large datasets.

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My Publications