As an interdisciplinary individual who dabbles in research, my work sits at the intersection of the environment, GeoAI, healthcare, and urban systems. I possess a persistent curiosity about how data, place, and equity interact. Influenced by my background in environmental studies, spatial analytics, and machine learning, I explore the design and applications of computational tools that make hidden risks visible.

My work integrates field-based sampling, remote sensing, computer vision, deep learning, spatial statistics, and civic technology. I've built systems based on Google Street View image segmentation to quantifying a variety of complex urban issues using spatial econometric modeling.

Before graduate school, I worked in telecommunications designing and splicing fiber optic cable routes in primarily suburban and rural areas, in cancer research biorepository preparing hazardous specimens and storing sensitive health data, and often in diverse local community advocacy. All of these experiences shaped my commitment to producing data and science that are technically rigorous and grounded in my lived experience and interactions.

Across every project I want to bring a reflective, systems-oriented approach: using data to predict, understand, explain, and intervene.