I design and build modeling systems that bridge statistical theory, simulation engines, and modern cloud platforms.
Quantitative Foundation
My background began in analytically rigorous quantitative research and econometrics, where I intentionally structured my graduate training around statistical modeling, regression analysis, and applied data analytics. That analytical foundation shaped my technical trajectory.
Modeling Systems Engineering
Over the past decade, I have developed nonlinear mixed-effects (NLME) modeling workflows, R packages, simulation systems, and optimization frameworks within pharmacometric software ecosystems. My work spans statistical computing, machine learning–assisted model search, workflow automation, and modeling engine integration.
Cloud & Full-Stack Engineering
More recently, I have expanded into full-stack and cloud engineering — contributing to next-generation cloud-native modeling platforms within enterprise pharmacometric software ecosystems. My work spans front-end architecture (React/TypeScript), backend services (Node/Express and Java-based systems), API integration, containerized infrastructure, and CI/CD pipelines supporting scalable modeling applications.
This work allows me to operate across the full delivery stack — translating complex modeling workflows into robust, production-grade cloud platforms.
Philosophy
I am particularly interested in complex systems where mathematics, optimization, and cloud software architecture intersect – building robust, production-grade modeling platforms that translate quantitative theory into usable, scalable tools.
Education
- MA, International Studies / Quantitative Political Analysis & Econometrics – Claremont Graduate University (2012–2014)
- BA, Political Science / International Relations & Game Theory – University of California, Riverside (2007–2011)
Languages
- English (native)
- Spanish (professional working proficiency)