As a research scientist and engineer, I seek to leverage machine learning, data science, computational modeling, and computer graphics to drive advancement in cutting edge engineering domains, such as LLM apps and semiconductors, by intuitively representing complex systems. I am a Lynn Fellow in the Computer Science Doctoral Program at Purdue University. My computational modeling research focusses on two major directions. First, I conduct research on the automated detection and analysis of semiconductor manufacturing defects using Machine Learning, Bayesian Experimental Design, and Quantum Sensing. Additionally, I develop high-dimensional computational models that leverage groundbreaking physics and graphics techniques for effective and intuitive space mission design. In May 2022, I received a double major in biomedical engineering and computer science along with a MSE in biomedical data science from Johns Hopkins University. At Johns Hopkins, I conducted research at the JHU Computational Cardiology Lab for the development of patient-specific MRI-derived computational heart models to assist clinical treatment of atrial fibrillation.