Stanford University doctoral student Zhecheng Wang discusses how he developed domain-tailored machine learning (ML) models and leveraged multi-modal geospatial data to construct Energy Atlas, a large-scale map overlay for distributed energy resources (DERs) and electricity infrastructures. Energy Atlas can address challenges in renewable energy adoption and climate change mitigation. Wang is a PhD student in civil and environmental engineering with a minor in computer science. His research is aimed at the development of AI-driven methods to provide closed-loop solutions for building sustainable urban and energy systems. He is co-advised by Stanford professors Ram Rajagopal and Arun Majumdar.

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