Topics include modeling with networks and graphs; linear, nonlinear, and integer programming, construction of models employing modern modeling languages; and general solution strategies.

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

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

Structure a complex problem through information gathering, data analysis, industrial engineering and design principles application, project management, while working in and leading diverse teams.

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. 

Methods for describing stochastic movements of material in manufacturing facilities, supply chain, and equipment maintenance networks. Includes analysis of congestion, delays, and inventory ordering policies.

Doctoral Thesis

Doctoral Thesis

Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.