Workshop
Julia, JuMP, and HiGHS
A hands-on solver session covering model formulation, decision variables, constraints, objectives, solution inspection, and debugging.
Teaching
I approach technical teaching as a translation problem: students should understand the model, see how it becomes code, and learn how to question the result.
Workshop
Python and PuLP
A practical introduction to building and solving linear and mixed-integer optimization models in Python.
Teaching philosophy
Students learn operations research most effectively when mathematical structure, computational implementation, and managerial interpretation remain connected. I emphasize transparent assumptions, validation, and communication rather than treating solver output as an answer by itself.
Course interests
Operations research, stochastic modeling, simulation, queueing theory, decision analytics, reinforcement learning for operations, engineering economics, and applied optimization.