Dr. Ruiwei Jiang obtained a BS in IE from Tsinghua University (Beijing) and a PhD in ISE from the University of Florida. He joined IOE and the University of Michigan in Fall 2015, after spending two years at the University of Arizona as an assistant professor. His research and teaching have mainly focused on the theory and methods of stochastic and discrete optimization, as well as their applications of societal importance, such as electric power systems, healthcare, and transportation systems. He has led the INFORMS Junior Faculty Interest Group (JFIG), chaired the INFORMS Computing Society Best Student Paper competition, co-chaired the INFORMS New Faculty Colloquium, and was a Vice Chair of the INFORMS Optimization Society. Dr. Jiang works on discrete optimization under uncertainty. Many practical engineering problems seek good discrete decisions under uncertain or even incomplete inputs. Jiang’s research aims to develop data-enabled stochastic optimization (DESO) models and solution methodology that bring together data analytics, integer programming, stochastic programming, and robust optimization. Together with his collaborators, Jiang applies DESO approaches to various engineering problems, including power and water system operations, transportation systems, and healthcare resource scheduling.
