Ruiwei works on discrete optimization under uncertainty. Many practical engineering problems search for good discrete decisions under uncertain or even incomplete inputs. Ruiwei’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, Ruiwei applies DESO approaches to various engineering problems, including power and water system operations, transportation systems, and healthcare resource scheduling.
The recognitions of Ruiwei’s work include a NSF CAREER award, two INFORMS Junior Faculty Interest Group paper awards, and an Outstanding Teaching award from the IISE Operations Research Division.