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Ruiwei Jiang obtained a BS in Industrial Engineering from Tsinghua University (Beijing) and a PhD in Industrial and Systems Engineering from the University of Florida. He joined the Industrial and Operations Engineering department at the University of Michigan in Fall 2015, previously spending years at the University of Arizona as an Assistant Professor. His research and teaching focus on theory and methods in stochastic and discrete optimization, with applications of societal importance in electric power systems, healthcare, and transportation systems. He has led the INFORMS Junior Faculty Interest Group and has been actively involved in various INFORMS competitions and colloquiums. His work emphasizes discrete optimization under uncertainty, aiming to develop data-enabled stochastic optimization models and methodologies that integrate data analytics with integer programming, stochastic programming, and robust optimization. Jiang collaborates with others to apply these approaches to real-world engineering problems, enhancing operations within power and water systems, transportation, and healthcare resource scheduling.
University of Arizona • Tucson, AZ
Taught courses and conducted research in Industrial and Systems Engineering.
University of Michigan • Ann Arbor, MI
Conducting research and teaching in Industrial and Operations Engineering.
Department of Electrical Engineering and Computer Science