Initially, my research interest centered on analyzing and modeling different kinds of omics data, including microarray data. Over time,my focus has shifted to building pipelines and developing algorithms to analyze and mine different kinds of next-generation sequencing (NGS) data, including RNA-seq, ChIP-seq, ATAC-seq, WGS-seq, WES-seq, RIP-seq, etc. Since 2016, I have led a team at the NIH National Institute of Environmental Health Sciences in developing a comprehensive pipeline for analyzing ultra-highthroughput single-cell sequencing data. This includes scRNA-seq, single-cell TCR-seq, scATAC-Seq, Single Cell Multiome ATAC, RNA-Seq, Total-seq, and Spatial Transcriptomes data. scRNA-seq is particularly significant, as it can reveal the unique properties of a single cell through its transcriptome, allowing us to address questions that bulk samples cannot. The technique is commonly used to determine population diversity, trace cell lineages, classify cell types, and identify rare cells. Unlike in bulk RNA-seq, this preserves the heterogeneity of cells (even of the same type). To date, I have authored or co-authored more than 60 peer-reviewed papers on this work.