School of Medicine


Showing 1-3 of 3 Results

  • Weiruo Zhang

    Weiruo Zhang

    Research Engineer, Biomedical Data Science

    BioDr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang obtained her M.S. and Ph.D. in Electrical Engineering, both from Stanford University. Her Ph.D. studies focused on developing computational algorithms for metabolomics data analysis, in which she received Young Scientist Award from the Metabolomics Society for her algorithm on metabolic network analysis delineating the effects of genetic mutants and drug treatment on the metabolome. Her postdoctoral studies at the Department of Radiology, Stanford School of Medicine, integrated radiomic data and genomic data that identified a prognostic metabolic regulation biomarker for non-small cell lung cancer. Her current research primarily focuses on developing and implementing novel computational methods to integrate and analyze single-cell multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. She has developed algorithms to solve computational challenges of spatial omics data and to identify mediators for cell-cell interactions associated with metastasis that was featured in Stanford Medicine Magazine. Dr. Zhang has authored and co-authored publications including Nature, Cell, Nature Methods etc. Her research aims at bridging multi-omics, imaging, machine learning, artificial intelligence to better understand biology for cancer progression and immunosuppression.

  • James Zou

    James Zou

    Associate Professor of Biomedical Data Science and, by courtesy, of Computer Science

    Current Research and Scholarly InterestsMy group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.

    We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.