Aaron joined MSEAS in the fall of 2019, starting his masters in Computational Science & Engineering (CSE) with plans to pursue a PhD in MechE-CSE. Broadly, his interests span stochastic differential equations and signal processing. He has begun focusing on numerical solutions to the acoustic wave equation in the presence of uncertainty in addition to Bayesian inference for acoustic inverse problems (see DEEP-AI). Furthermore, he works on developing and improving reduced-order modeling techniques by incorporating differential geometry for applications in uncertainty quantification. Before coming to MIT, Aaron graduated with a Bachelor of Science from Brown University, double concentrating in applied mathematics and engineering, where he researched terahertz optics phenomena. Outside of academia, he enjoys running, water skiing, hiking, and low-level soccer.