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About Me:
I am a tenure-track assistant professor in the Department of Biostatistics at MD Anderson Cancer Center. I received my Ph.D. from the Department of Biostatistics at the University of Michigan, under supervision of Prof. Xiang Zhou, focusing on developing statistical methods for genetic and genomic datasets. I also worked closely with Prof. Jennifer Smith in the Department of Epidemiology at the University of Michigan on large-scale quantitative trait mapping in African Americans in the GENOA study. Prior to that, I obtained my bachelor’s degree in Biology from the Zhiyuan Honored College at Shanghai Jiao Tong University.
Research Interests:
My research interest is in developing effective and efficient statistical and machine learning methods in single cell and spatial transcriptomics to address critical biological problems such as omics data dimension reduction and integration. My specific focus includes: 1). developing statistical and computational methods for data analysis in single-cell RNA-seq and spatial transcriptomics; 2). Bridging between population-level genome-wide association studies (GWASs) and individual-level single-cell and spatial transcriptomics data to unravel the underlying mechanisms of disease etiology; 3). Integrating multi-omics data with patient outcomes to foster translational research, ultimately connecting molecular insights with clinical applications.
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Funding
Selected Awards
Yuchen received the Aurelia Murphy Killian Endowment in Public Health Award! Congratulations to Yuchen!
We are grateful to MDA for awarding us the Institutional Research Grant, which will support the development of new methodologies for spatial omics data analysis. Our proposal was selected as one of the top 20 among all applications at MDA.
Our paper ““Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics”” is accepted at Nature Communications! Will be online soon.
Our GENOA meQTL paper is accepted at Nature Communications! Will be online soon.
Our SRTsim paper is online at Genome Biology! Here is the link: SRTsim paper.