Phebe Vayanos

Main Content

Teaching


  • Present 2016

    Introduction to Operations Research: Deterministic Models, ISE 330 (UG level, ~20 students)

    University of Southern California, Industrial & Systems Engineering

    This course is a basic introduction to important models and solution methods in Industrial and Systems Engineering (ISE). ISE is concerned with the modeling, analysis, and solution of complex decision problems that arise in the management or design of a large-scale industrial system such as a supply chain, transportation network, or manufacturing assembly line. This course will focus specifically on the modeling and solution of linear programs, dynamic programs, and integer programs, as well as additional applications thereof in transportation, logistics, supply chain management, among others.

  • Present 2015

    Linear Programming, ISE 631 (Ph.D. level, ~15 students)

    University of Southern California, Industrial & Systems Engineering

    Taught with rigor, this is the first doctoral course in the field of optimization that serves as the foundation for all subsequent courses in the broad area of mathematical programming. As such, the course is intended for first-year Ph.D. students and advanced M.S. students who intend to pursue a doctoral degree.

  • 2014

    Optimization Methods, 15.093 (M.S. level, ~80 students)

    MIT Sloan School of Management, Operations Research & Statistics Group

    Introduces the principal algorithms for linear, network, discrete, robust, nonlinear, dynamic optimization and optimal control. Emphasizes methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods.

  • 2013

    Mixed-Integer and Non-Convex Optimization, 10.557 (Ph.D. level, ~30 students) - Guest Lecturer

    Massachusetts Institute of Technology, Department of Chemical Engineering

    Taught the part of the course concerned with mixed-integer optimization. Introduced modelling with binary variables (binary choice, relations between events, forcing constraints, logical propositions, disjunctive constraints, etc.), strength of formulations, and the cutting planes algorithm.

  • 2014

    Robust Modeling, Optimization & Computation, 15.094 (Ph.D. level, ~40 students)

    MIT Sloan School of Management, Operations Research Center
    2011 2009

    Operations Research, CO343 (M.Eng. level, ~80 students)

    Imperial College London, Department of Computing
  • 2011

    Computational Finance, CO422 (M.Eng. level, ~130 students)

    Imperial College London, Department of Computing