The emerging data-rich environments in healthcare hold great promises to accelerate the paradigm transition of U.S. healthcare from reactive care to preventive care. One question is how we could translate the big disease data into better care management of preclinical or diseased patients. While these diseases manifest complex progression process, involving both temporal dynamics and spatial evolution, how could we model, monitor, and modify these processes are challenging problems. The challenges mainly lie on three aspects: disease modeling, monitoring, and prognosis. For example, diseases such as Alzheimer’s disease and Type 1 Diabetes share the commonality that they involve slow and predictable progression processes. Knowing how a disease progresses is helpful, particularly if we’d like to prevent the disease as early as we could for maximum therapeutic e cacy and improved quality of life. The modeling of the progression process is statistically chal- lenging given the high-dimensionality of the data (e.g., tens of thousands variables), the mixed types variables, and the data’s longitudinal nature. Another commonality of these diseases is that, since they are chronic condi- tions, being able to recognize subtle symptoms that indicate signi cant clinical events or suggest worse outcomes is crucial for preventative care. Further, patients need to be dynamically prioritized by their projected risk for resource allocation optimization. This needs robust models that build on the statistical knowledge provided by disease modeling and monitoring, to guide the selection of high-risk patients for targeted care. Thus, my works collectively work towards the goal of smart monitoring. Such a smart monitoring method will provide data-driven decision-making capabilities for better disease management, leading to e cient targeted screening and a ordable care, better treatment planning, and improved quality of life for both patients and caregivers.