Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems.

This course focuses on analysis of high-dimensional structured data including profiles, images, and other types of functional data using statistical machine learning. A variety of topics such as functional data analysis, image processing, multilinear algebra and tensor analysis, and regularization in high-dimensional regression and its applications including low rank and sparse learning is covered. Optimization methods commonly used in statistical modeling and machine learning and their computational aspects are also discussed. 

In-depth modeling and analysis of the workflow dynamics shaping the operation and performance of production and service systems. Application of stochastic modeling in practical problems.

Placeholder

Independent doctoral research conducted under the supervision of the faculty advisor, leading to the development, execution, and completion of the student’s dissertation research.

Placeholder

For graduate students holding graduate research assistantships.

Deterministic models of supply chains including location and material flow. Optimization techniques including linear programming, network flows, integer programming, and heuristics.