My research interests are the development and application of statistical methods for analyzing high throughput omics data to better understand how key interactions between genes (or proteins) contribute to human diseases. My academic training and research experience have provided me with a strong integrated background in biostatistics, machine learning, and biology. I have received interdisciplinary training including immunology, molecular biology, statistics methods in genomics, network inference, bioinformatics, and deep learning. Recently I focuses on developing statistical methods for constructing protein-protein networks associated with phenotype and developing Graph Convolutional Neural Network algorithms in the field of genomics for classification. To date, my colleagues and I have developed several statistical methods including a novel tissue augmented Bayesian model for eQTL analysis, sparse multiple canonical correlation network analysis (smCCNet), and an augmented high-dimensional graphical Lasso method to incorporate prior biological knowledge for global network learning (ahGlasso).