Jorge E. Pesantez

Jorge E. Pesantez serves as an Assistant Professor in the Civil and Geomatics Engineering Department at California State University Fresno's Lyles College of Engineering. He holds a Ph.D. and M.S. in Civil Engineering from North Carolina State University (2021, 2017), a Master's in Construction Management from the University of the Armed Forces (ESPE), Ecuador (2014), and a B.S. in Civil Engineering from the University of Cuenca, Ecuador (2010). Prior to joining Fresno State in 2022, he completed postdoctoral research at the University of Illinois Urbana-Champaign.

Dr. Pesantez's research focuses on water distribution systems analysis, demand management models, and machine learning applications for urban infrastructure. His work integrates agent-based modeling (using AnyLogic and Python) with hydraulic simulation to address challenges in leak detection, peak demand shifting, and infrastructure resilience. Notable projects include developing intelligent leak detection frameworks and persuasive gaming mechanisms for demand management through advanced metering infrastructure.

His publication portfolio demonstrates significant contributions to water resources engineering, with Editor's Choice awards from the American Society of Civil Engineers Journal of Water Resources Planning and Management in 2020 and 2022. Current research trends emphasize neural network applications for water demand forecasting, genetic algorithms for network rehabilitation, and digital twin implementations for pandemic impact analysis.

• Editor's Choice Collection Paper Award, Journal of Water Resources Planning and Management (June 2022)
• Editor's Choice Collection Paper Award, Journal of Water Resources Planning and Management (August 2020)

Dr. Pesantez actively secures competitive research funding as Principal Investigator, including a $167,933 Bureau of Reclamation grant for intelligent leak detection systems and multiple California Water Institute awards. His teaching portfolio includes computational methods, hydraulics, and construction engineering courses such as CE 80 Computer Applications in Civil Engineering and CE 128 Hydraulics. He leads projects analyzing California Central Valley emergency evacuation and developing deep learning models for traffic congestion forecasting.