Iam Associate Professor of Pathology at Tulane University School of Medicine and has more than 20 years’ experience on bioinformatics,including analyses of microarray, NGS, proteomics and single cell sequencingdata. Before I joined Department of Pathology and Lab Medicine in Tulane University in 2022,I has worked as senior bioinformatics scientist for ten years at NationalInstitutes of Environmental Health Sciences, NIH. During that time, I took aleadership in bioinformatics core to use thecutting-edge computational tools and innovative algorithms to analyze spatialtranscriptome/proteomics data and multi-omics single cell data, includingscRNA-seq, scATAC-seq, CITE-Seq, TCR/BCR-seq, Perturb-seq and SpatialTranscriptomics. I have analyzed more than 1000 samples of scRNA-Seq, more than60 samples of spatial transcriptome sequencing and 40 other type of single cellsequencing data for more than 15 type of tissues including lung, gut, bone,stomach, testes, pancreas, PBMCs, heart, blood vessels, brain, kidney, skin,and liver, which led to more than 15 high impact papers and 10 manuscriptsunder review so far.
Researchinterest
1) Develop newbioinformatics algorithms and pipelines. New tools are made to design,integrate and interpret large molecular data sets, including next generationsequencing, single cell RNA-seq and spatial transcriptome data. Last couple ofyears, we focused on the cutting-edge computational tools to analyzesingle-cell genomic sequencing data and have published several papers on highprofile journals (e.g., Nature Biotechnology and Brief in Bioinformatics)through collaboration. My recent research focuses on new algorithms andsoftware to analyze spatial transcriptome/proteomics data and multi-omicssingle cell data, including scRNA-seq, scATAC-seq, CITE-Seq, TCR/BCR-seq,Perturb-seq and Spatial Transcriptomics.
2). Understandhow cellular heterogeneity (temporal and spatial) encodes the molecularstructure, function, and regulation of complex biological systems. We apply bioinformatics algorithms andpipelines to biology data through collaboration with both basic and clinicalresearch scientists. We have been working on more than 30 projects usingsingle cell sequencing and spatial transcriptomics technology, includingStomach cancer, lung cancer, colorectal cancer, skin cancer, leukemia, braintumor, liver cancer etc. Recently, we are applying powerful tools instatistical inference and machine-learning (ML/AI) to Spatial Transcriptomicsand spatial proteomics data, especially cancer/tumor and clinical data.
