Research Interests
My lab builds data driven, computational systems to analyze high-resolution histology images of diseased tissue (e.g. digitized clinical H&E/IHC slides or multiplex immunofluorescence imaging) as well as other clinical data sources. Our aim is to develop artificial intelligence approaches that improve diagnostic precision, increase access to state of the art care for patients around the world, uncover novel biomarkers for disease prognosis/therapeutic response, and streamline the basic scientific investigation of disease processes. A major focus of our work is the development of novel machine learning approaches to address the scale and complexity of histopathology images, which are typically 10s-100s of thousands of pixels in dimension and take years of clinical training to interpret. Analyzing these tissue-based data modalities and linking them 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.
List of publications from Google Scholar
