(734) 834-2165
Applications: Cloud Computing Operations Management, Critical Infrastructures, Cyber Defense, Health Care, Intelligent Transportation Methodologies: Discrete Optimization, Machine Learning, Stochastic Optimization Relevant Projects:
  • NSF
  • IBM
  • PG&E
  • DoE (pending)

Siqian Shen

Assistant Professor, Industrial and Operations Engineering

The research of Shen’s group covers the following aspects that are closely related to data science and decision making.

  1. We develop modeling techniques and computational paradigms for complex service systems that incorporate a variety of information and decisions. We design risk optimization approaches that are capable of handling integrated system design and service operations with multiple resources, multiple stages of service, and multiple stakeholders with diverse decision preferences under data uncertainty.
  2. 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. We have implemented the related approaches in a series of research projects that optimize stochastic problems of (i) project management, (ii) resource allocation, (iii) appointment scheduling, and (iv) network interdiction. Moreover, 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.
  3. We study network interdiction models and design algorithms for specially structured networks (e.g., trees, small-world networks) in defense-related problems. Dynamic programming and heuristic approaches are used for deriving solutions in polynomial time, and generating valid bounds to the corresponding stochastic optimization models.
  4. With increasing penetrations of fluctuating renewable energy sources, such as wind power plants and solar photovoltaics, and active participation of electric loads in power system operation, uncertainty will increase. Specifically, we develop data-driven and distribution-free 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. The flexibility of an aggregation of loads is difficult to compute and uncertain. Investigating, characterizing, and managing this uncertainty is the focus of this research. Moreover, we quantify the tradeoff between the uncertainty and profitability of load control, and the effect of uncertainty and methods for managing it on power system dispatch, which affects pollutant emissions.
A supply-chain network for transportation and logistics.

A supply-chain network for transportation and logistics.