Research Expertise and Interest

applied statistics, functional genomics, translational bioinformatics, high dimensional and integrative genomic/genetic data analysis, network modeling, hierarchical multi-lable classification

Research Description

As an applied statistician, my research is at the interface between statistics and data-rich scientific disciplines. Especially, my group is interested in taking on a variety of data modeling and analysis challenges from the cutting edge of biological and medical research, and developing effective new methods to answer complex biological and medical questions. Specific topics that our group works on include:

1). Bioinformatics: (a) Prediction of functional elements in genome sequences; (b) Identification of gene functions, relationships, pathways and networks using diverse genomic data, for example gene expression data, regulatory data, and snp data; (c) Analysis of next-generation high-throughput sequencing data for epigenomic studies, bio-marker discovery, and de novo transcript identification.

2). Translational bioinformatics: Integrative information retrieval from diverse sources for (a) aiding disease diagnosis, (b) predicting disease-drug relationships, and (c) revealing drug side effects.

3). Statistics: network modeling, clustering, hierarchical multi-label classification, distributional approximations, statistical optimization.