About me.

I completed my undergraduate studies in Tropical Biology, where I first began working with digital imagery and data to study ecological and environmental systems. During this period, I started using R for data analysis and co-founded an R user group at the Universidad Nacional de Costa Rica, organizing workshops and teaching data analysis to students and researchers.

After my undergraduate degree, I worked professionally as a data developer at ixpantia, developing data-driven tools and software products for organizations across Latin America and the United States. My work there involved building end-to-end solutions, including predictive models, automation pipelines, APIs, and production-ready analytical applications, with a strong emphasis on reproducibility, documentation, and long-term maintainability.

I later pursued an MSc in Earth and Atmospheric Sciences, where my work focused on remote sensing and Earth observation. Using satellite imagery and geospatial datasets, I studied environmental processes and uncertainty in remotely sensed measurements. This phase of my work was centered on large-scale, passive observation and the interpretation of spatial patterns in environmental systems.

I am currently pursuing a PhD in Civil and Environmental Engineering, where my work centers on perception-driven systems for emergency response. I focus on building autonomous and semi-autonomous systems that combine deep learning–based computer vision with geospatial context under real-world operational constraints, including human-in-the-loop decision-making.

Alongside my research, I contribute to and maintain open-source scientific software, including pygeostat, and I participate in open science and community initiatives such as rOpenSci and Data Latam. I am also engaged in applied-AI professional development through Amii’s AI Career Accelerator Program.