ESE 605: Modern Convex Optimization

Graduate Course, University of Pennsylvania, 2020

This second year graduate course introduces students to the theory needed to recognize and solve convex optimization problems, particularly those that arise in engineering. The first half of this course covers essential theory including convex analysis, the identification of linear, quadratic, geometric, and semidefinite programs, and duality. The second half of the course emphasizes the practice of transforming a broad range of engineering objectives into convex programs such as statistical estimation, approximation, and control as well standard first and second order algorithms to solving both unconstrained and constrained convex optimization programs.

As a teaching assistant, my responsibilities including grading homeworks for the course of about sixty students, writing solution keys, and hosting four hours of office hours per week. Additionally, in order to improve retention I gave optional review lectures covering the necessary mathematical prerequisites for the course, primarily linear algebra. Due to the disruption of the Spring 2020 semester by COVID-19, I assisted Professor Nikolai with the extra administrative burden as well as added additional early morning office hours to accomodate students in different time zones.