Dr. Sun is a Postdoctoral Scholar in the Department of Bioengineering at Stanford University, working under the mentorship of Dr. Emma Lundberg. He earned his Ph.D. in Computational Biology at Carnegie Mellon University, advised by Dr. Robert F Murphy.
His research interests span computational biology, biostatistics, and statistical learning. Dr. Sun's current work focuses on developing image-derived generative models of biological systems across multiple scales, from cellular to tissue levels. These models are designed to characterize organizational variations across different regions and individuals. Additionally, Dr. Sun maintains an active interest in the theoretical foundations of modern machine learning systems.
Huangqingbo (Paul) Sun

sunh at stanford edu

Department of Bioengineering
Stanford University
443 Via Ortega
Stanford, CA 94305

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Publications

⁺ equal contribution, * corresponding author
Preprints
  • Intrinsic Diversity in Primary Cilia Revealed Through Spatial Proteomics
    Jan Hansen, Huangqingbo Sun⁺, Konstantin Kahnert⁺, Alexandra Johannesson, Kalliopi Tzavlaki, Casper Winsnes, Emmie Pohjahnen, Jennny Fall, Mathias Uhlen, Ulrika Axelsson, Frederic Ballllosera Navarro, Anna Backstrom, Cecilia Lindskog, Fredric Johansson, Kalle von Feilitzen, Angelica Delgado Vega, Anna Martinez Casals, Diana Mahdessian, Anna Lindstrand, Eini Westenius, Emma Lundberg*
    bioRxiv (2024).
    [paper]
  • Flexible and robust cell type annotation for highly multiplexed tissue images
    Huangqingbo Sun, Shiqiu Yu, Anna Martinez Casals, Anna Bäckström, Yuxin Lu, Cecilia Lindskog, Emma Lundberg*, Robert F Murphy*
    bioRxiv (2024).
    [paper] [software]
Peer-reviewed papers
  • Expanding the coverage of spatial proteomics: a machine learning approach
    Huangqingbo Sun, Jiayi Li, Robert F Murphy*
    Bioinformatics, 40, no. 2 (2024): btae062.
    [paper] [software]
  • Basal body organization and cell geometry during the cell cycle in Tetrahymena thermophila
    Huangqingbo Sun, Adam Soh, Lisa Mitchell, Chad Pearson, and Robert F. Murphy*
    Molecular Biology of the Cell, 34, no. 6 (2023): ar53.
    [paper] [software]
  • Improving and evaluating deep learning models of cellular organization
    Huangqingbo Sun⁺, Xuecong Fu⁺, Serena Abraham, Shen Jin, and Robert F. Murphy*
    Bioinformatics, 38, no. 23 (2022): 5299-5306.
    [paper] [software]
  • Evaluation of categorical matrix completion algorithms: toward improved active learning for drug discovery
    Huangqingbo Sun, and Robert F. Murphy*
    Bioinformatics, 37, no. 20 (2021): 3538-3545.
    [paper] [software]
  • An Improved Matrix Completion Algorithm For Categorical Variables: Application to Active Learning of Drug Responses
    Huangqingbo Sun, and Robert F. Murphy*
    ICML 2020 Workshop on Real World Experiment Design and Active Learning, (2020).
    [paper]
  • A MEMS variable optical attenuator with ultra-low wavelength-dependent loss and polarization-dependent loss
    Huangqingbo Sun, Wei Zhou, Zijing Zhang, and Robert F. Murphy*
    Micromachines, 9, no. 12 (2018): 632.
    [paper]
Book chapters & technical reports
  • Learning Morphological, Spatial, and Dynamic Models for Cellular and Subcellular Components
    Huangqingbo Sun, and Robert F. Murphy*
    Imaging Cell Signaling, Methods in Molecular Biology, 2800. Humana, New York, NY.
Plain Academic