Yutong Wang (王雨桐)

Yutong Wang (王雨桐)

Postdoctoral Researcher

University of California, San Francisco

Biography

I am a postdoctoral scholar with Dr. Jimmie Ye at University of California, San Francisco. I recently completed my PhD in Biostatistics at University of California, Berkeley, working with Dr. Yun S. Song. My research is primarily driven by developing and applying machine learning and statistical methods to understand biology. Recently, I am studying how genes interact with each other in the multi-cellular organisms.

As a first-generation college student, I am 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 led many initiatives to increase accessibility and representation. Materials from my guest lectures of the Biostatistics seminar course at UC Berkeley can be found here, about eugenics in statistics, and algorithmic fairness.

Pronouns: she/her/hers

Interests

  • ML/AI
  • High Dimensional Statistics
  • Representation Learning
  • Probabilistic Modeling
  • Spatial Biology
  • Immunology

Education

  • Ph.D. in Biostatistics (designated emphasis in Computational and Genomic Biology), 2020 - 2024

    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

Publications

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

PDF DOI

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.