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Stochastic Modeling and Systems

Stochastic modeling is built upon probability theory, statistics, and stochastic processes to address uncertain, complex physical, cyber, and service systems. Our fundamental research in this area includes modeling systems, analyzing the system performance with respect to input uncertainty, and validating output against real-world uncertain outcomes. The research in IE spans a whole host of applications in supply chains and healthcare systems.

This area includes:

  • Reinforcement Learning
  • Queuing Theory
  • Simulation
  • Applied Probability

Faculty in this Area:

Tugce IsikAmin KhademiJeffrey P. KharoufehQi LuoHamed RahimianYongjia Song, Kevin M. Taaffe,  Emily Tucker, Dan Li

  

 

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