I graduated from Purdue University in the Spring of 2025. My passion is using numerical linear algebra, exploiting floating point arithmetic, and parallel algorithms to make high-consequence problems in computational science and AI solvable.
An exploration of Multilevel Monte Carlo (MLMC) for estimating expectations of stochastic differential equations, using the Ornstein–Uhlenbeck process as a test case. I investigate how approximate random variables and mixed floating-point precision influence computational efficiency and accuracy.
A deep dive into using FGMRES, an iterative solver enhanced with reinforcement learning, for efficient portfolio optimization.
A quantitative walkthrough of building a ranked stock screener based on disaggregated price using Finviz data and the DuPont Identity.