Courses in special topics of timely interest to the profession, conducted by resident or visiting faculty.

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. 

Convex sets, theory of finite systems of linear inequalities, convex functions, convex programming.

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.

Introduction to probability, probability distributions, point estimation, confidence intervals, hypothesis testing, linear regression, and analysis of variance. Crosslisted with MATH 3770 and CEE 3770. Also, credit not awarded for both ISYE 3770 and MATH 3670.

Doctoral Thesis

Doctoral Thesis