CCMB/SCSAP Joint Seminar: Mingyao Li, PhD
3330 Auditorium, Medical Science Building
ZOOMAbout This Event
CCMB/SCSAP Joint Seminar by Mingyao Li, PhD
"Digital tissue twins: turning histology into spatial omics at population scale"
Abstract
Spatial omics technologies are reshaping how we study the molecular organization of tissues, offering unprecedented opportunities to map biology in its native spatial context. However, key challenges remain in resolution, scalability, and accessibility. In this talk, I will describe how my lab is developing Al-powered approaches that integrate spatial omics with histopathology to overcome these barriers. I will highlight computational strategies that make spatial omics experiments smarter, faster, and more informative, from enhancing spatial resolution beyond current experimental limits, to extending analyses across large and complex tissues, to integrating diverse molecular measurements within a unified framework. Together, these approaches enable the creation of "digital tissue twins," computational representations of tissues that capture both molecular and structural organization. Finally, I will discuss our efforts to design Al systems that are transparent and trustworthy, ensuring that these tools can support robust biological discovery and clinical translation. These advances aim to unlock the full potential of spatial omics and enable new insights in the era of precision medicine.
CCMB/SCSAP Joint Seminar: Mingyao Li, PhD
Presenters
Mingyao Li, PhD
Professor of Biostatistics in Biostatistics and Epidemiology
Senior Scholar, Center for Clinical Epidemiology and Biostatistics
Director of Biostatistics
Director, Statistical Center for Single-Cell and Spatial Genomics
University of Pennsylvania Perelman School of Medicine
Dr. Mingyao Li joined the Biostatistics faculty in 2006. She is also a faculty member of the Genomics and Computational Biology graduate program. Her main research area is statistical genetics and genomics. The central theme of her current research is to use statistical and machine learning 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 diseases, age-related macular degeneration, Alzheimer's disease, chronic kidney disease, type 1 diabetes, and cancer. 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.
Host/Moderator
Cong Ma, PhD
Assistant Professor
