In summer 2025, I was the graduate student instructor (i.e., lead instructor) for ISyE 4133 (Advanced Optimization). This course covers the "theory and implementation of practical methods to find good or optimal solutions to optimization problems too large or complex to solve in a straightforward way."

Details can be found below:

• Course syllabus (link).
• Taught 11 upper-level undergraduate students.
• Score of 4.33/5 on "Considering everything, the instructor was an effective teacher" (Full score available upon request).
• Implementations were consistently used to reinforce theoretical concepts and applications from lecture. 14/15 lectures had case studies with implementation exercises. 10/14 were newly designed, ranging from linear to nonlinear to large-scale optimization, shown below:
  
  • L1: Solving a supply chain problem with Gurobi (link)
  • L3: Solving a random diet problem (link)
  • L5: Duality theory for economic dispatch (link)
  • L8: Nonlinear optimization for regression (link)
  • L9: Intro to PyTorch for regression (link)
  • L10: Intro to gradient descent (link)
  • L11: Visualizing rates of convergence (link)
  • L14: L-shaped method for the random diet problem (link)
  • L15: Dantzig-Wolfe for multi-period economic dispatch (link)
  • L16: A generalized implementation of Dantzig-Wolfe (link)
• Two new course projects were designed to provide students with hands-on experience with cutting-edge software, and students chose based on preference:
  • Project 1: Large-scale integer programming for circuit design (link)
  • Project 2: Machine learning for predicting cancer diagnoses (link)
  Solutions and rubric are available upon request.