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TITLE: "Steady-state diffusion approximations in service systems: engineering solutions and error bounds
ABSTRACT:
Steady-state diffusion approximations are commonly used to approximate models of large scale service systems. In this talk I will introduce a framework based on Stein's method that
can be used a) as an engineering solution for generating good steady-state approximations
and b) as a mathematical tool for establishing error bounds for these approximations. These approximations are often universally accurate in multiple parameter regions, from underloaded, to critically loaded, to overloaded (when customers abandon). As a running example, I will use the many server queue with customer abandonment and phase-type service time distribution,
which is a fundamental building block in service system models.
Bio: Anton is currently a Ph.D student at Cornell University in the Operations Research Department working with Professor Jim Dai. He received a Bachelors degree in Math and Statistics from the University of Toronto. Broadly speaking, He is interested in stochastic modeling and applied probability. His thesis work focuses on applying Stein's method to the study of steady-state approximations of stochastic systems. These approximations include both first order approximations such as mean-field/fluid approximations, and second order approximations such as diffusion approximations. However, he is also interested in Markov decision processes and stochastic control theory. With respect to application domains, he is interested in fleet management questions in ridesharing systems such as Uber, Lyft, or Didi.
