Mingyao Li, PhD

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Mingyao Li, PhD

Professor of Biostatistics

Dr. Li joined the biostatistics faculty in 2006. She is also a faculty member of the Genomics and Computational Biology (GCB) graduate program, and holds a secondary appointment in the Department of Statistics at Wharton. Her main research area is statistical genetics and genomics, bioinformatics and computational biology. The central theme of her current research is to use statistical and computational approaches to understand cellular heterogeneity in human disease relevant tissues, to characterize gene expression diversity across cell types, and to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell transcriptomics studies. In addition to methods development, Dr. Li is also interested in collaborating with researchers seeking to identify complex disease susceptibility genes. Her collaborative research includes cardiometabolic disease, age-related macular degeneration, Alzheimer's disease, chronic kidney disease, type 1 diabetes, cancer, and gene therapy for rare diseases. Findings from her research will seed cell-specific functional studies, in vivo modeling, and precision therapeutic targeting of human diseases. Dr. Li actively serves in the scientific community. She is an Associate Editor of Statistics in Biosciences, and was a regular member of the Genomics, Computational Biology and Technology study section and a member of the review committee of the Center for Inherited Disease Research of the NIH.

Content Area Specialties: 
  • Cardiometabolic disease
  • Age-related macular degeneration
  • Alzheimer's disease
  • Chronic kidney disease
  • Type 1 diabetes
  • Cancer
  • Gene therapy for rare diseases
Methods Specialties: 
  • Statistical genetics and gene mapping of complex diseases
  • Genome-wide association and next-generation sequencing
  • Statistical methods for RNA sequencing data analysis
  • Statistical methods for single-cell genomics
  • Applications of deep learning in single-cell genomics
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