Yi Zhang

Neurosurgery, Neuro-Oncology

Assistant Professor of Neurosurgery

Yi Zhang Profile Photo
Yi Zhang Profile Photo

Research Interests

Machine Learning Methods for Omics
We develop computational methods leveraging interpretable machine learning models, large-scale single-cell genomics data, and multi-omic datatypes like spatial transcriptomics and multiomics. 

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Multi-Cellular Tumor Microenvironment
Tumor, like many multi-cellular disease systems, are composed of multiple cell types. For solid tumor, cancer-intrinsic properties like proliferation, mutations, and epigenetic changes, and cancer-extrinsic properties like tumor-infiltrating immune cell states and inflammation, both affect tumor progression and therapeutic responses. We aim to understand molecular gene programs of tumor microenvironment (TME) cell states, pinpoint functional cancer-TME interactions, and identity targetable tumor immunity modulators. We are core members of the Brain Tumor Omics Program at Duke Preston Robert Tisch Brain Tumor Center. We will use our computational expertise to develop methods that allow us to understand the incurable tumor types and to improve cancer therapy efficacy.

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Integrating Human Genetics and Functional Genomics
Human genetic variants are natural probes to investigate cell context-dependent gene regulation related to human disease. Genome-wide association studies (GWAS) identified many genetic variants associated with cancer susceptibility. We are interested in building computational methods to find cell-dependent effect of genetic variants by integrating GWAS summary statistics and functional genomics.

Bio

I am an Assistant Professor at Duke University as primary faculty in Department of Neurosurgery and secondary in Department of Biostatistics and Bioinformatics. My passion sits at the intersection of computational method development and biomedical and genomics data. I did PhD in Bioengineering at University of Illinois at Urbana-Champaign and postdoc at Dana-Farber Cancer Institute and Harvard University School of Public Health. We have been developing integrative computational genomic methods to identify functional gene regulatory mechanisms behind disease-associated human genetic variants, machine learning methods that leverage large-scale single-cell genomics data to understand cell states in tumor. My lab at Duke focuses on computational biology, bioinformatics, and machine learning in genomics. Our research interest includes developing interpretable machine learning methods for patient-based single-cell, spatial transcriptomics, and multi-omics data, and also building integrative genomics methods that combines and functional genomics, to understand multi-cellular systems like tissues and tumor microenvironment, and to finally enable translational and biomedical discoveries. 

Education

  • Ph.D. University of Illinois, Urbana-Champaign, 2019

Positions

  • Assistant Professor of Neurosurgery
  • Assistant Professor of Biomedical Engineering
  • Assistant Professor in Biostatistics & Bioinformatics
  • Member of the Duke Cancer Institute

Awards, Honors, and Distinctions

  • EECS Rising Star 2022. University of Texas Austin. 2022

Publications