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Renee Obringer is an interdisciplinary researcher who works at the nexus of data science, urban systems, and climate change. Her research projects range from using machine learning to model urban reservoir levels to analyzing the impact of climate change on the water-energy nexus. Renee earned her PhD in environmental and ecological engineering from Purdue University in 2020. She was also a member of the Ecological Science and Engineering Interdisciplinary Graduate Program.
At SESYNC, Renee uses a combination of machine learning and agent-based modeling to better understand the relationship between urban residents and anthropogenic droughts. She is interested in the impact people may have on improving or exacerbating existing droughts, as well as the feedback loops that may develop over time. Currently, she is considering the Southwestern United States as a case study, but plans to develop a broad methodological technique that could be applied across the world.