My teaching approach starts from intuition and a concrete decision problem, then moves toward mathematical formulation, analysis, and implementation.
Developed and taught courses in Foundations of Reinforcement Learning, Mathematical Optimization, and Mathematics for AI, including lectures, code examples, exercises, and project materials.
I prefer to begin with a system that students can reason about intuitively, such as waiting lines, inventory decisions, allocation problems, or sequential decisions under uncertainty. From there, I introduce the mathematical abstraction, work through the analysis, and return to interpretation and implementation.
For students with different mathematical backgrounds, I use visual explanation, small hand-worked examples, and computational experiments before moving to more formal derivations. The goal is not only to apply a formula, but to understand what the model assumes, what its result means, and where it may fail.
Probability, stochastic processes, queueing models, and performance evaluation.
Mathematical modeling, optimization, simulation, and decision analysis.
Congestion, capacity, user behavior, and operational design.
Forecasting, machine learning, reinforcement learning, and their role in operational decisions.