Data mining, mutual information and evolutionary genetics

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My research is in the areas of statistical methods in data mining and evolutionary genetics. The first part includes Multivariate data exploratory methods, data reduction,   visualization and applying information theory to improve the data mining techniques. The methodologies developed are suitable for a variety of applications such as marketing research, financial data, medical data, microarray data and environmental data. My research in evolutionary genetics focuses on  statistical methods for analysis of codon evolution. Such models are extremely important, as they allow the estimation of an index of selection pressure which has valuable applications to wide-ranging topics such as vaccine design, drug discovery and understanding the evolution of bacterial pathogenicity. The intersection of my two areas of research is on the emerging field of statistical genomics, contributions to topics here have important impacts in many areas of biology and society.