Juan is a data scientist specializing in advanced geospatial techniques, with a particular interest in leveraging his skills in geographic information systems (GIS), machine learning, and simulation modeling to improve urban environments.
He holds a Master of Science degree in Urban Spatial Science with distinction from University College London (UCL), where his dissertation - developed in partnership with the United Nations - focused on creating deep learning tools for the automated detection of precarious areas in developing countries through satellite imagery. He also holds an Honours Bachelor of Science degree from the University of Toronto.
Juan joined ALLFED in the fall of 2023 as a research intern and transitioned to a research associate role by 2024. In this capacity, he provides expertise in geospatial data analysis, programming, and cartography. His latest research project at ALLFED explores the impact of abrupt sunlight reduction scenarios on critical infrastructure, particularly underground systems like water distribution networks.
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