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Gabriel is interested in developing computational models for data-driven urban policymaking, focusing on spatial problems that foster equity and livability in contemporary cities. He works with Professors Emma Pierson and Nikhil Garg.
With my background in Mathematics, I have been exposed to rather abstract concepts which I would like to bring into the urban planning and social sciences discussion. I believe that the rigorous description of society through Statistics, Topology, or Graph Theory can help us understand better what are inherent properties of urban space and what can be improved. I am especially drawn to studying cities of the Global South as well as underserved neighborhoods and the mathematical constraints of urbanism for these populations, and to solving problems in public policy using Machine Learning with unreliable or sparse data—unfortunately, those interests very often intersect.
With my background in Mathematics, I have been exposed to rather abstract concepts which I would like to bring into the urban planning and social sciences discussion. I believe that the rigorous description of society through Statistics, Topology, or Graph Theory can help us understand better what are inherent properties of urban space and what can be improved. I am especially drawn to studying cities of the Global South as well as underserved neighborhoods and the mathematical constraints of urbanism for these populations, and to solving problems in public policy using Machine Learning with unreliable or sparse data—unfortunately, those interests very often intersect.
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AAAI 2024no. 20 (2024): 21888-21896
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