Teaching

IE 5357 - Computer Simulation Applications (Spring 2025)

Graduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2025

Course Description: This course introduces the different concepts and methods of simulation. Students investigate using discrete-event simulation (DES) to solve complex systems such as healthcare or manufacturing processes. The course emphasizes the fundamental concepts and interpretation of results from process and agent-centric models. The course includes topics such as 1) basic concepts of modeling and simulation, 2) Queuing theory, 3) statistical distribution, 4) working with simulation software, 5) solving case studies, 6) healthcare systems modeling, 7) Statistical result analysis of simulation output, 8) Presenting simulation using Graphical User Interface (GUI), and 9) Model verification and validation. This course is offered separately for graduate and undergraduate students in an alternative semester. The graduate students learn advanced modules of programming simulation logic.

Class Enrollment: 6 graduate students enrolled in this course.

Student Evaluation: Not available yet.

IE/MFG/SE 5390 - Special Topics course (Quality Engineering Boot Camp) (Spring 2025)

Undergraduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2025

Course Description: This course was developed in collaboration with an instructor from Lockheed Martin Aeronautics (LMA). This course offers an in-depth exploration of fundamental principles, methodologies, and tools crucial for enhancing product and process quality. This intensive 10-day Quality Engineering Bootcamp provides an accelerated introduction to the principles and practices underpinning quality engineering, delivering valuable insights and preparing participants to significantly impact industries’ quality landscape. The course content includes: 1) statistical process control, 2) DMAIC, FMEA, Product/process verifications, software quality engineering, foreign object damage/debris prevention for aerospace and defense industries, 3) seven basic QC tools, 4) Lean and six sigma applications, 5) Model-based quality engineering, and 6) NDT/E and advanced quality inspection procedure. Students also complete an exciting real-work project from LMA. This is a cross-listed course for both undergraduate and graduate students, and graduate students are required to lead the course project.

Class Enrollment: 9 seniors and 10 Graduate students enrolled in this course.

Student Evaluation: Not available yet.

IE/MFG/SE 5358 - Industrial Data Analytics (Fall 2024)

Graduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2024

Course Description: This course serves as an introduction to data analytics, focusing on its application in industrial settings. The curriculum is structured to introduce students to the fundamentals and complexities of data mining through a series of modules: (1) an overview of data analytics, (2) preprocessing of industrial data, (3) mining frequent patterns and correlations, (4) part classification, (5) cluster analysis, (6) exploration of data mining software, and (7) practical application of data analytics techniques using Python. This course equips students with both a foundational understanding of general data analytics methods and the skills to apply these techniques to solve industry-specific problems. This is a cross-listed course for both undergraduate and graduate students, and graduate students are required to do a course project.

Class Enrollment: 22 Graduate students enrolled in this course.

Student Evaluation: 4.4/5.0

IE 4353 - Industrial Systems Simulation (Fall 2024)

Undergraduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2024

Course Description: This course introduces the different concepts and methods of simulation. Students investigate using discrete-event simulation (DES) to solve complex systems such as healthcare or manufacturing processes. The course emphasizes the fundamental concepts and interpretation of results from process and agent-centric models. The course includes topics such as 1) basic concepts of modeling and simulation, 2) Queuing theory, 3) statistical distribution, 4) working with simulation software, 5) solving case studies, 6) healthcare systems modeling, 7) Statistical result analysis of simulation output, 8) Presenting simulation using Graphical User Interface (GUI), and 9) Model verification and validation. This course is offered separately for graduate and undergraduate students in an alternative semester. The students learn advanced modules of programming simulation logic.

Class Enrollment: 45 seniors enrolled in this course.

Student Evaluation: 5.0/5.0

IE 5357 - Computer Simulation Applications (Spring 2024)

Graduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2024

Course Description: This course introduces the different concepts and methods of simulation. Students investigate using discrete-event simulation (DES) to solve complex systems such as healthcare or manufacturing processes. The course emphasizes the fundamental concepts and interpretation of results from process and agent-centric models. The course includes topics such as 1) basic concepts of modeling and simulation, 2) Queuing theory, 3) statistical distribution, 4) working with simulation software, 5) solving case studies, 6) healthcare systems modeling, 7) Statistical result analysis of simulation output, 8) Presenting simulation using Graphical User Interface (GUI), and 9) Model verification and validation. This course is offered separately for graduate and undergraduate students in an alternative semester. The graduate students learn advanced modules of programming simulation logic.

Class Enrollment: 5 graduate students enrolled in this course.

Student Evaluation: N/A

IE/MECH 4395 & IE/SE/MECH 5390 - Special Topics course (Quality Engineering Boot Camp) (Spring 2024)

Undergraduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2024

Course Description: This course was developed in collaboration with an instructor from Lockheed Martin Aeronautics (LMA). This course offers an in-depth exploration of fundamental principles, methodologies, and tools crucial for enhancing product and process quality. This intensive 10-day Quality Engineering Bootcamp provides an accelerated introduction to the principles and practices underpinning quality engineering, delivering valuable insights and preparing participants to significantly impact industries’ quality landscape. The course content includes: 1) statistical process control, 2) DMAIC, FMEA, Product/process verifications, software quality engineering, foreign object damage/debris prevention for aerospace and defense industries, 3) seven basic QC tools, 4) Lean and six sigma applications, 5) Model-based quality engineering, and 6) NDT/E and advanced quality inspection procedure. Students also complete an exciting real-work project from LMA. This is a cross-listed course for both undergraduate and graduate students, and graduate students are required to lead the course project.

Class Enrollment: 8 seniors and 7 Graduate students enrolled in this course.

Student Evaluation: 5.0/5.0

IE 4353 - Industrial Systems Simulation (Fall 2023)

Undergraduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2023

Course Description: This course introduces the different concepts and methods of simulation. Students investigate using discrete-event simulation (DES) to solve complex systems such as healthcare or manufacturing processes. The course emphasizes the fundamental concepts and interpretation of results from process and agent-centric models. The course includes topics such as 1) basic concepts of modeling and simulation, 2) Queuing theory, 3) statistical distribution, 4) working with simulation software, 5) solving case studies, 6) healthcare systems modeling, 7) Statistical result analysis of simulation output, 8) Presenting simulation using Graphical User Interface (GUI), and 9) Model verification and validation. This course is offered separately for graduate and undergraduate students in an alternative semester. The students learn advanced modules of programming simulation logic.

Class Enrollment: 35 seniors enrolled in this course.

Student Evaluation: 4.69/5.0

IE 4395 & IE/MFG/SE 5390 - Special Topics course (Industrial Data Analytics) (Spring 2023)

Undergraduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2023

Course Description: This course serves as an introduction to data analytics, focusing on its application in industrial settings. The curriculum is structured to introduce students to the fundamentals and complexities of data mining through a series of modules: (1) an overview of data analytics, (2) preprocessing of industrial data, (3) mining frequent patterns and correlations, (4) part classification, (5) cluster analysis, (6) exploration of data mining software, and (7) practical application of data analytics techniques using Python. This course equips students with both a foundational understanding of general data analytics methods and the skills to apply these techniques to solve industry-specific problems. This is a cross-listed course for both undergraduate and graduate students, and graduate students are required to do a course project.

Class Enrollment: 13 seniors and 10 Graduate students enrolled in this course.

Student Evaluation: 4.58/5.0

IE 5357 - Computer Simulation Applications (Spring 2023)

Graduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2023

Course Description: This course introduces the different concepts and methods of simulation. Students investigate using discrete-event simulation (DES) to solve complex systems such as healthcare or manufacturing processes. The course emphasizes the fundamental concepts and interpretation of results from process and agent-centric models. The course includes topics such as 1) basic concepts of modeling and simulation, 2) Queuing theory, 3) statistical distribution, 4) working with simulation software, 5) solving case studies, 6) healthcare systems modeling, 7) Statistical result analysis of simulation output, 8) Presenting simulation using Graphical User Interface (GUI), and 9) Model verification and validation. This course is offered separately for graduate and undergraduate students in an alternative semester. The graduate students learn advanced modules of programming simulation logic.

Class Enrollment: 15 graduate students enrolled in this course.

Student Evaluation: 4.64/5.0

IE/MFG/SE 5390 - Special Topics course (Industrial Data Analytics) (Fall 2022)

Graduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2022

Course Description: This course serves as an introduction to data analytics, focusing on its application in industrial settings. The curriculum is structured to introduce students to the fundamentals and complexities of data mining through a series of modules: (1) an overview of data analytics, (2) preprocessing of industrial data, (3) mining frequent patterns and correlations, (4) part classification, (5) cluster analysis, (6) exploration of data mining software, and (7) practical application of data analytics techniques using Python. This course equips students with both a foundational understanding of general data analytics methods and the skills to apply these techniques to solve industry-specific problems. This is a cross-listed course for both undergraduate and graduate students, and graduate students are required to do a course project.

Class Enrollment: 22 Graduate students enrolled in this course.

Student Evaluation: 4.67/5.0

IE 4353 - Industrial Systems Simulation (Fall 2022)

Undergraduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2022

Course Description: This course introduces the different concepts and methods of simulation. Students investigate using discrete-event simulation (DES) to solve complex systems such as healthcare or manufacturing processes. The course emphasizes the fundamental concepts and interpretation of results from process and agent-centric models. The course includes topics such as 1) basic concepts of modeling and simulation, 2) Queuing theory, 3) statistical distribution, 4) working with simulation software, 5) solving case studies, 6) healthcare systems modeling, 7) Statistical result analysis of simulation output, 8) Presenting simulation using Graphical User Interface (GUI), and 9) Model verification and validation. This course is offered separately for graduate and undergraduate students in an alternative semester. The students learn advanced modules of programming simulation logic.

Class Enrollment: 31 seniors enrolled in this course.

Student Evaluation: 4.67/5.0

IE 4395 & IE/MFG/SE 5390 - Special Topics course (Industrial Data Analytics) (Spring 2022)

Undergraduate course, The University of Texas at El Paso, Department of Industrial, Manufacturing and Systems Engineering, 2022

Course Description: This course serves as an introduction to data analytics, focusing on its application in industrial settings. The curriculum is structured to introduce students to the fundamentals and complexities of data mining through a series of modules: (1) an overview of data analytics, (2) preprocessing of industrial data, (3) mining frequent patterns and correlations, (4) part classification, (5) cluster analysis, (6) exploration of data mining software, and (7) practical application of data analytics techniques using Python. This course equips students with both a foundational understanding of general data analytics methods and the skills to apply these techniques to solve industry-specific problems. This is a cross-listed course for both undergraduate and graduate students, and graduate students are required to do a course project.

Class Enrollment: 19 seniors and 19 Graduate students enrolled in this course.

Student Evaluation: 4.4/5.0

Md Fashiar Rahman, Ph.D.

Teaching