Daniel Che

Daniel Che is an Associate Professor in the Department of Civil and Environmental Engineering at Ohio University's Russ College of Engineering and Technology. He joined the faculty in 2016 and specializes in water resources engineering and fluid mechanics. His research focuses on computational optimization models for hydraulic infrastructure design and real-time flood management systems.

Che holds a Ph.D., M.S.E., and B.S.E. in Civil Engineering from Arizona State University (2015, 2011, 2009). He previously worked as a water resources engineer at Atkins North America, specializing in Python-driven hydrologic modeling. His awards include the 2025 Russ Outstanding Teaching Award and multiple White Departmental Awards for teaching and research.

Research interests span real-time flood forecasting, reservoir operation optimization, machine learning applications, and sustainable urban water systems. His work integrates simulation and optimization tools to address water infrastructure challenges, with projects funded by agencies like ODOT and the National Academies of Sciences. Recent studies include rock channel design verification and sustainable water allocation in India's Wardha Sub-Basin.

Che's 15 most recent publications (2014–2024) emphasize flood control modeling, reservoir optimization, and machine learning for hydraulic engineering. He has authored reports on temporary stream facilities, fish passage prioritization, and green infrastructure placement. His lab collaborates with institutions like the University of Michigan and Ohio River Basin Consortium.