PhD
Assistant Professor, Pathology
Assistant Professor, School of Data Science and Society
UNC-Chapel Hill
Cancer Epidemiology
Area of Interest
My lab builds data driven, computational systems to analyze high-resolution histology images of diseased tissue and other clinical data sources. Artificial intelligence aided diagnosis in Pathology has the potential to improve clinical practice for treating cancer by increasing diagnostic precision, reducing the costs of care, speeding up clinical workflows, and making diagnostic expertise accessible to more patients. Computational approaches also have the potential to advance the state of care by facilitating the discovery of novel biomarkers for disease prognosis and therapeutic response as well as streamlining basic scientific investigation of disease processes.
The major focus of our research is analysis of the large amounts of high-resolution spatial-omics data that are starting to be generated in Pathology laboratories such as digitized H&E stained slides, multiplex immunofluorescence, spatially resolved transcriptomics, and 3D histology images. The scale and complexity of these data (e.g. whole slide images are typically 10s-100s of thousands of pixels in dimension and take years of medical training to interpret) often mean new algorithms need to be developed to fully harness the promise of these emerging tissue measurement technologies. Linking these different tissue-based data modalities together with other molecular, radiology, and electronic health record information presents significant opportunities for discovery, but also poses statistical and computational challenges.
We work to solve these difficult biomedical data analysis problems by weaving together domain expertise, deep-learning, computer vision, statistical inference, and open-source software.
