Statistical learning: methodology and efficient algorithms

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The objective of my research is to develop efficient supervised and unsupervised statistical learning tools for the analysis of data from drug discovery and survey sampling. My research on drug discovery focuses on inventing supervised learning methods to capture the complex structure of drug discovery data and identify rare active compounds (rare target problem) as potential drug candidates. Statistical learning in drug discovery seeks to build a model that uses descriptors characterizing molecular structure (explanatory variables) to predict biological activity (response variable). However, the characteristics of drug discovery data can make it difficult to model the relationship between molecular descriptors and biological activity. A supervised learning method based on the mixture model with the consideration of the measurement error of descriptors will be developed to deal with above challenges. This involves model selection determining the number of components in the mixture models, measurement error modeling and development of efficient computer algorithms. My research on the sample survey will focus on inventing unsupervised statistical learning methods to cluster sample survey data with ordered categorical variables and a large amount of missing values. I will focus on clustering observations when variables measured may be ordered categorical with missing values. a model-based clustering algorithm will be developed in Bayesian framework.