Paulina Rodriguez

I am a computational scientist specializing in verification, validation, and uncertainty quantification (VVUQ) for engineering applications. My work focuses on developing and evaluating credible, reproducible computational models that support decision-making under uncertainty.

My path into computational science has been non-linear, beginning in downtown Los Angeles and leading to my current work as a PhD candidate in the Department of Mechanical and Aerospace Engineering at The George Washington University. As a first-generation college student, I earned a Bachelor’s degree in Mathematics from the University of California, Santa Cruz, followed by a Master of Science in Mathematics from Claremont Graduate University. During my undergraduate years, I explored mathematical modeling through undergraduate STEM programs, where I developed a strong foundation in computational approaches to scientific problems.

Early in my career, I worked as a program manager for the CalTeach Program at UCSC and as a web developer, where I built experience in software development, coding, and system-level problem solving. At the U.S. Food and Drug Administration (FDA), I applied these skills to evaluate computational models for medical device applications, contributing to the assessment of model credibility for regulatory decision-making. My current research focuses on developing an end-to-end example of a regulatory-grade computational model for medical device applications, with an emphasis on risk-informed credibility and reproducibility.

As a fellow of the Department of Energy Computational Science Graduate Fellowship (CSGF), my work explores the intersection of high-performance computing, uncertainty quantification, reproducibility, and open-source software. Through my practicum experiences at the Sandia National Laboratories, I have applied Verification, Validation, and Uncertainty Quantification methods to multiphysics modeling problems, including the development of multi-metric approaches for validation under limited data conditions.

My broader goal is to advance credible and reproducible modeling and simulation practices and to bridge the gap between computational modeling, experimental data, and decision-making.

As a first-generation Chicana scientist, I value building spaces where rigorous science and personal identity are not separate, but integrated. I am committed to contributing to a scientific community that is both technically strong and accessible.

If you’re interested in my work or would like to connect, feel free to reach out. I’m always happy to share perspectives and learn from others working to build more credible and reproducible science.