Graph-based Data Analytics
[Lecture Notes] Fall 2023
Data analytics and machine learning (ML) on graphs. Node, edge, and graph embedding, representation learning. Descriptive network analysis and traditional ML on graphs. Graph neural networks. Codes for network analysis and ML. Contemporary topic student presentations. Course project with hands on application.
Operations Research I
[Syllabus] Spring 2023
Techniques to optimize and analyze industrial operations. Use of linear programming, transportation models, networks, integer programming, goal programming, dynamic programming, and non-linear programming.
Quantitative Modeling
[Syllabus] Fall 2022
Applications modeling and optimization methods. Domains: transportation, logistics, manufacturing, service systems scheduling, and supply-chain management. Decision models: linear programming and sensitivity analysis, transportation and assignment, network models and algorithms, and integer, dynamic and nonlinear programming.