Prior to joining SCARP, Madison worked in nuclear physics, applied mathematics, and data science. Now, she seeks to meaningfully apply her technical experience to making urban systems more sustainable, understanding the interconnectedness of infrastructure, people, and the environment, and identifying how our urban designs have left certain groups without access to basic services. She aspires to bring data-driven analysis to policymakers built intrinsically from, and with consideration of, the community members affected.
Identifying Paths to Sustainable Transportation Through Machine Learning
A reliance on vehicle travel represents an unsustainable future both ecologically and economically. While advances in technology have led to lower emission vehicles, including electric and hybrid vehicles, reaching the desired goals of a net zero carbon world by 2050 will require a reduction in vehicle use altogether. Pulling in work from physics, computer science, and psychology, her research seeks to quantify an individual’s potential of switching transportation modes and use this to inform intervention strategies tailored to the people affected by the change.
