Xinyi Zhang PhD

Xinyi Zhang will develop machine learning models to both advance the understanding of biological mechanisms and facilitate the discovery of therapeutic targets in diseases, building on theoretical and empirical advances in machine learning and causal inference. Xinyi studied Bioengineering and Computer Science at the University of California, Berkeley, and earned her PhD in Computer Science at the Massachusetts Institute of Technology, where she was advised by Prof. Caroline Uhler and supported by a graduate fellowship from the Eric and Wendy Schmidt Center at the Broad Institute.

During her PhD, she developed computational frameworks that integrate diverse modalities—including spatial transcriptomics, chromatin and protein staining to achieve a comprehensive view of cell state and tissue organization in diseases. Her work identified region-specific progression patterns, chromatin biomarkers, and gene expression changes in Alzheimer’s disease, and revealed shared spatial re-organization across multiple neurodegenerative disorders. To scale to large clinical cohorts, she developed representation learning methods that extract rich morphological information from simple and cost-effective imaging assays. Her models also enable prediction of missing modalities, such as the subcellular localization of unmeasured proteins at single-cell resolution. Most recently, she has worked on disentangling the shared and modality-specific information across multiple modalities to better understand the underlying regulatory mechanisms and inform experimental design.