Mitchel Colebank, UC Irvine: Cardiovascular disease is the leading cause of death in the modern world and acts on multiple spatial and temporal scales. Computational models have had notable success in simulating cardiovascular function and integrating multimodal data from either pre-clinical or clinical studies. The development of subject-specific models informed by these data sources are necessary for establishing cardiovascular digital twins for clinical patient management. However, functional data (e.g., invasive hemodynamics) as well as structural imaging data are both subjected to measurement error but are necessary for model calibration and parameter inference. Thus, cardiovascular digital twins must include mathematical models of multiscale, physiological mechanisms, as well as robust statistical methods for parameter inference and uncertainty quantification. Surrogate modeling is also necessary to overcome the computational expense of these multiscale models and enable nearly real time predictions. In this talk, I will discuss innovations in image-based models of blood flow (described by partial differential equations), multiscale systems-level models of cardiac function (systems of ordinary differential equations), and the statistical tools necessary for inverse problems and uncertainty quantification in cardiovascular research. While a majority of the work will focus on pulmonary vascular and right heart function, these methods collectively build the necessary tools for developing digital twins for multiple cardiac and vascular subunits of the full cardiovascular system.