Data mining, mutual information and evolutionary genetics

我的研究领域是数据挖掘和进化遗传学中的统计方法。第一部分包括多元数据探索方法，数据简化， 可视化和应用信息论来改进数据挖掘技术。开发的方法适用于各种应用，如市场研究，金融数据，医疗数据，微阵列数据和环境数据。我在进化遗传学方面的研究主要集中在密码子进化分析的统计方法上。这样的模型是非常重要的，因为它们允许选择压力的指数的估计有价值的应用广泛的主题，如疫苗设计，药物发现和理解细菌致病性的演变。我的两个研究领域的交叉点是统计基因组学的新兴领域，对这里的主题的贡献在生物学和社会的许多领域都有重要影响。

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.