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Ph.D. Student in Civil and Environmental Engineering, admitted Winter 2015
BioKim Quesnel is a PhD candidate in the Civil and Environmental Engineering department where she is co-advised by Dr. Newsha Ajami and Dr. Richard Luthy through the NSF Engineering Research Center for Reinventing the Nation’s Urban Water Infrastructure (ReNUWIt) and Stanford’s Water in the West program.
Kim’s research investigates urban water demand as a key component of advancing future water supply planning. Her work focuses on uncovering the drivers of urban water demand using modern computational tools. As part of this work, she has looked at how media coverage and coupled public awareness of the recent historic California drought were related to changes in water use behavior. She is also using high resolution data from smart water meters to model demand at the customer level. Additionally, motivated by the water sector’s chronic fiscal challenges, Kim is researching novel approaches to water financing and governance that can help to increase innovation in the water sector. Her research has been covered by the LA Times, Scientific American, High Country News, Water Deeply, and other news outlets.
Prior to coming to Stanford, Kim worked as a civil engineer in Denver, Colorado in the field of environmental remediation, responsible for both technical design work and project management. She has also worked on a wide range of water-related research projects including the laboratory investigation of tsunami wave breaking behaviors at the O.H. Hinsdale Wave Research Laboratory in Oregon, the assessment and design of water filtration in rural Thailand, and the study of glacier hydrology through field research in Alaska. Kim received a B.S. in Civil Engineering from California Polytechnic State University, San Luis Obispo and an M.S. in Civil and Environmental Engineering, Environmental Fluid Mechanics and Hydrology from Stanford University. She was awarded an Environmental Protection Agency (EPA) STAR fellowship for her research on urban water demand forecasting.