Jiang obtained a Bachelor degree from the Department of Industrial Engineering at the Tsinghua University, and a Ph.D. from the Department of Industrial & Systems Engineering at the University of Florida.
Ruiwei Jiang's research focuses 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, renewable energy integration, and healthcare resource scheduling.
Cutting Planes for the Security-Constrained Coal-Fired Stochastic Unit Commitment, with Y. Guan and J.-P. Watson. Mathematical Programming. Forthcoming, 2016.
Data-Driven Chance Constrained Stochastic Program, with Y. Guan. Mathematical Programming. Forthcoming, 2015.
Weekly Two-Stage Robust Generation Scheduling for Hydrothermal Power Systems, with H. Dashti, A. J. Conejo, and J. Wang.
IEEE Transactions on Power Systems. Forthcoming, 2015. Robust Unit Commitment With Wind Power and Pumped Storage Hydro, with J. Wang and Y. Guan.
IEEE Transactions on Power Systems. 27(2): 800-812. 2012.