Associate Professor, Industrial and Operations Engineering
The research of Shen’s group covers the following areas that are closely related to data science and large-scale optimization problems.
– We develop new decomposition paradigms for stochastic integer programming models. We focus on two-stage stochastic integer programs, and advance decomposition paradigms based on special structures of specific risk-averse programs, and also based on special integer-programming structures.The new decomposition paradigms can be widely applied to large-scale complex system design and operations management, including optimizing critical interdependent infrastructures such as power grids, transportation systems, and cyber-clouds.
– We optimize carsharing system design/operations and real-time ridesharing problems including supply-demand matching for ride-pooling as well as service pricing.
– We apply risk-averse models and approaches to optimize integrated system design and service operations with multiple resources, multiple stages of service, and multiple stakeholders with diverse decision preferences. We in particular focus on related problems in healthcare operations management.
– We develop data-driven optimization methods that are suited to dispatching power systems with both fluctuating renewable energy sources and flexible loads contributing to balancing reserves via load control. We also study multi-stage stochastic programs over various risk and robustness measures for transmission planning with complex spatio-temporal data correlations.
– We study network interdiction models and design algorithms for specially structured networks (e.g., trees, small-world networks) in defense-related problems.