Doctoral Research

My dissertation, The Governance of Invasive Species Management: A Computational Social Science Approach, examined how people understand public policies through cause-and-effect reasoning.

The project grew from a longstanding challenge in policy research. Prominent theories of the policy process argue that shared beliefs shape how actors form coalitions, interpret problems, and seek to influence government action.

Yet researchers have struggled to measure those belief systems systematically.

So I began with a practical question: How can policy researchers and analysts measure policy beliefs from real-world evidence using large language models (LLMs)?

To address that question, I developed the Theory of Causal Policy Beliefs, which explains policy reasoning through four connected elements: perceived problems, their causes, desired outcomes, and proposed interventions. I paired the theory with an AI-assisted method grounded in ontology engineering, which I called Simonizer.

I applied this framework to the governance of biological infestations in the American Southwest, examining how stakeholders understood environmental problems, evaluated proposed interventions, and learned within complex institutional settings.

Publications based on this research are in development.

Government Experience

Previous government experience provided firsthand insight into decision-making in highly dynamic settings.

Before pursuing my doctorate, I spent five years advising the Government of Costa Rica and public-sector organizations across the housing sector.

I worked closely with ministerial leadership on policy analysis, political risk assessment, institutional reform, and negotiations between government agencies and the national legislature.

Teaching

I look forward to returning to the classroom and teaching research methods again.

I taught public policy and methodology courses at the University of Costa Rica. I also supervised senior undergraduate theses through the School of Political Science's capstone research seminar.

I designed and taught graduate-level case study methodology at the Central American Institute of Public Administration, training public administration professionals in qualitative research design.

At the University of Arizona, I served as instructor of record for undergraduate courses in public administration and criminal justice.

Service

Data literacy and programming education were central to my service at the University of Arizona.

I served as a Data Science Ambassador, designing and delivering workshops that introduced data science methods and programming tools to interdisciplinary audiences.

I joined the Roots for Resilience program as a fellow, where I received advanced training in open science and reproducible research practices before leading workshops for graduate students.

I am also a certified Carpentries Instructor and co-author of a forthcoming open-access book on R programming.

Earlier Training and Writing

I was trained as a qualitative social scientist before crossing over to quantitative and computational methods.

I have been borrowing ideas from both traditions ever since.

Before moving to the United States to pursue my PhD, I earned a Master's degree in Public Policy from the Latin American Faculty of Social Sciences (FLACSO) in Quito, Ecuador, and a Bachelor's degree in Political Science from the University of Costa Rica.

Along the way, I also wrote occasional opinion pieces for Divergentes, one of Central America's leading independent news magazines.