PROJECT SUMMARY/ABSTRACT The bone marrow is the primary site of hematopoiesis and its examination is central to the diagnosis and management of patients with hematological diseases. Pathology slides from clinical hematopathology services represent a treasure trove of real-world data on the biology of human bone marrow. However, human examination is time-intensive and limited in its quantitative precision, preventing our ability to perform large- scale studies or identify new morphologic markers of disease. Machine learning and image processing methods can be applied to the analysis of whole-slide images (WSIs), which will lead to improvements in our understanding of the hematological system, as well as our ability to diagnose and manage hematological diseases. The objective of this research is to build deep learning-based tools for the automated classification of bone marrow aspirates and use these tools to study hematopoiesis in hematopoietic stem cell transplant recipients. The central hypothesis is that automated methods can be developed for the classification, characterization, and quantification of cell morphology on human bone marrow aspirates and that these tools can identify morphologic features predictive of outcome after hematopoietic stem cell transplant. The long-term goal is to develop a suite of artificial intelligence tools for the quantitative analysis of hematopathology WSIs that can be used to improve our understanding of hematopoiesis and our management of patients with hematologic diseases through the development of digital hematopathology tools, stains, and biomarkers for research and clinical applications. This approach is innovative because it applies cutting-edge image analysis technology to the quantitative and scalable study of human bone marrow specimens from clinical pathology archives to drive discovery of new biological insights and clinical biomarkers.