Teaching
Teaching
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I have taught undergraduate classes for 10+ semesters at both Pontificia Universidad Catolica de Chile (PUC) and Georgia Tech.
2026 (1 semester) - Georgia Tech
2026 (1 semester) - Georgia Tech
I am the instructor of the class ISYE3044 Simulation and Analysis Design: "Discrete event simulation methodology emphasizing the statistical basis for simulation modeling and analysis. Overview of computer languages and simulation design applied to various industrial situations."
Details of the class:
Two sections: in-person and asynchronous
Total of 47 students
Both theory and practical contents.
Contents
Contents
The contents include random numbers generation, random variates generation, fitting input distributions, static and dynamic simulation, output analysis, model building, validation and verification of the model, analysis of steady state simulations.
In addition, the last part of the class includes a practical approach to model building using SIMIO Software, where we cover Discrete-Event Simulation. The students end the class developing a project based on real-world scenarios.
2018 - 2022 (9 semesters) - PUC
2018 - 2022 (9 semesters) - PUC
I was the instructor of the class ICS1113 Optimization: "To train students in mathematical modeling for decision-making using optimization techniques, including linear, non-linear, continuous, and integer programming, as well as network flow problems."
Summary of the class among all semesters:
The modality was originally in person, which need to be adapted during 2020-2021 (online for three semesters and hybrid for one semester).
Total of 800+ students
Both theory and practical contents
Contents
Contents
The contents include modeling (review of classic problems), review of basic concepts (optimal solution, feasibility, convexity), linear programming (graphical solution, simplex, sensitivity analysis, duality), integer programming (basic concepts, branch and bound, cuts), network flow (modeling, simplex, shortest path, max flow) and nonlinear programming (gradient descent, Lagrange and KKT conditions).
In addition, students must develop an optimization model of a real-world problem chosen by them. This allows students to face the challenges in building a model, such as understanding a real process, collecting information, solving a medium/large scale optimization problem and making business insights.
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