I define myself as a mathematical engineer and computational scientist.My work focuses on advancing our understanding of physical and biological phenomena, improving the design of engineering systems, and supporting informed decision-making under uncertainty by use of mathematical/statistical modeling and high-performance computing. An important component of my work is the systematic integration of mathematical models and data (e.g., experimental measures and images) using the Bayesian framework. Throughout my career, I have led the development of novel formulations and algorithms for the solution of both forward and inverse problems. I have achieved this by establishing a close working collaboration with some of the leading research experts in the fields of medical imaging, geophysics, engineering, computational sciences, and applied mathematics.During my doctoral program and postdoctoral training, I made several important contributions to the field of computational science and engineering, and more specifically with regards to the use of large-scale numerical simulation as a tool to inform decision-making under uncertainty. These included the use of patient-specific computational hemodynamics simulations to quantify wall shear stress and predict the risk of rupture of an aneurysm; the optimal control of the inlet condition of a turbulent jet to ensure proper mixing; and the use of information-theoretic approaches (expected information gain) to optimally designing sensing systems.As a Research Assistant Professor at Washington University, I am working on several projects aiming at advancing quantitative and functional image reconstruction algorithms with the ultimate goal of advancing the state-of-care for cancer diagnosis and treatment. In particular, my focus is on emerging acoustic and optical imaging modalities, such as photoacoustic computed tomography (PACT) and ultrasound computed tomography (USCT).