This document discusses computational pathology research. It describes using computational methods like high dimensional fused informatics, image analysis, and machine learning to analyze pathology images and integrate them with genomic and clinical data. The goals are to characterize tumors at multiple scales, predict treatment outcomes, and identify tumor subtypes. Challenges include managing the large amounts of image and multi-dimensional data generated. The document outlines several of Joel Saltz's pathology research projects and computational pathology initiatives like challenges that integrate radiology, pathology, and genomic data to predict patient outcomes.