Teaching

Course Description: Introduction to fundamental optimization models and solution methods, including linear programs, the simplex method, duality theory, sensitivity analysis, integer programs, and network flows.

Book Link: Introduction to Operations Research: 2024 Release ISE

Course Description: Data analysis, regression, model selection, regularization, classification (logistic regression, SVM, KNN, NN, DT), and unsupervised learning/clustering (e.g., K-means)  

Book Link: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Course Description: Introduction to the theory of linear programming and duality, algorithms for solving linear programs (e.g., simplex, network simplex, column generation), integer and mixed integer programming modeling, algorithms for solving integer/mixed integer programs (e.g., branch and bound, and branch and cut).

Book Link: Integer Programming