IE 432 Stochastic and Risk-Sensitive Optimization

STARS'ta hâlâ Quantitative Risk Management diye geçiyor.

Decision-making under uncertainty is the through-line here: you learn to quantify "how bad can it get" rigorously rather than relying on expected value alone, using tools like VaR, coherent risk measures, stochastic dominance, and extreme value theory. The work is a mix of problem sets, a project, and a report where you build stochastic optimization models (two-stage, multi-stage, MDPs, chance-constrained) and apply them to finance, energy, and operations problems. It sits downstream of IE's stochastic modeling and optimization sequence and is the natural bridge to quant finance, energy planning, and operations research roles where ignoring tail risk is the difference between a model that ships and one that blows up.