Research Interests: To harness tools from computational pathology and machine learning to analyze the spatial content of a primary tumor, to predict the most probable mechanisms of metastatic progression and to guide therapy. To this end, we are developing objective and quantitative measures for cellular and sub-cellular spatial patterns between cellular phenotypes in transmitted light tissue sections and between panels of cancer biomarkers (up to 60 fluorescently labelled antibodies and nucleic acid probes in the same tissue section) from patient tumor samples.