Yutong Wang (王雨桐)

Yutong Wang (王雨桐)

PhD candidate in Biostatistics

University of California, Berkeley


I am a PhD candidate in the Graduate Group in Biostatistics at University of California, Berkeley, advised by Prof. Yun S. Song. Committed to advancing our understanding of biology through rigorous computational and statistical frameworks, I specialize in distilling insights from high-dimensional and noisy biomedical data. My methodological focus encompasses statistical machine learning and deep learning techniques, which I apply to a wide array of biological questions spanning cell biology, cancer immunology, and neuroscience. I am particularly captivated by the untapped potential of emerging technologies such as spatial transcriptomics and CRISPR-screening, which I believe are key to unraveling the complex molecular landscapes underlying tumor microenvironments and cellular interactions.

As a first-generation college student, I am deeply devoted to promoting diversity, equity, inclusion, and belonging (DEIB) in academia. Through various roles, including serving as the Inaugural DEIB Fellow at UC Berkeley’s Biostatistics Division, I have spearheaded numerous initiatives to increase accessibility and representation. My efforts span from community outreach programs exposing underrepresented minorities to STEM, to lectures exploring algorithmic bias in healthcare, to bolstering inclusive admissions processes. Having directly experienced educational barriers, my personal journey fuels my passion for dismantling systemic inequities. I strive to continue driving positive change, furthering DEIB through mentorship, collaboration, and outreach.

Pronouns: she/her/hers


  • Statistical Machine Learning
  • High Dimensional Statistics
  • Probabilistic Modeling
  • Statistical Genetics
  • Spatial Transcriptomics
  • Immunology


  • Ph.D. in Biostatistics (designated emphasis in Computational and Genomic Biology), 2020 - present

    University of California, Berkeley

  • M.A. in Biostatistics, 2018 - 2020

    University of California, Berkeley

  • B.S. in Mathematics and Applied Mathematics, 2014 - 2018

    Tianjin University, China


(2021) XYZeq: Spatially-resolved single-cell RNA-sequencing reveals expression heterogeneity in the tumor microenvironment. Science Advances.


Recent & Upcoming Talks

Single-cell and spatial transcriptomics data analysis with Seurat in R

A thorough walk-through is provided to perform computation and data analysis on single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics data using Seurat and other packages in R. Other topics include the explanation of a general Seurat object, and the conversion of sequencing data formats between R and Python.