Zhen Miao

I develop statistical and machine learning methods to understand cellular regulation from single-cell, spatial, and sequence data. I am a postdoctoral researcher in the Matsen Lab at Fred Hutch Cancer Center and the Howard Hughes Medical Institute. My current work combines antibody language models with explicit models of mutation and selection to study B-cell receptor evolution.

I received my PhD in Genomics and Computational Biology and my MA in Statistics and Data Science from the University of Pennsylvania in 2024. I worked with Junhyong Kim and Nancy Zhang and continued at Penn as a postdoctoral researcher through 2025.

My research connects models of biological variation with models of how assays measure it:

• Spatial coordination. CoPro identifies gene programs that vary together across neighboring cells and tissue sections. My recent theory paper studies the population targets, identifiability, and stability of spatial kernel restricted canonical correlation analysis.
• Chromatin accessibility. Paired-Insertion Counting (PIC) provides consistent quantification of single-nucleus ATAC-seq data. PACS tests the effects of multiple biological factors on accessibility while accounting for incomplete capture.
• Development and disease. I apply these methods to understand cell-state changes and tissue organization, including kidney development and repair. With collaborators, I helped map the single-cell regulatory landscape of the mouse kidney.

My software is available on GitHub. You can find my CV and publications here.