Title: Data-driven Risk-aware Adversarial Analysis under Uncertainty

We study stochastic sequential games with the players’ being risk averse. We focus on specific network interdiction applications and develop data-driven optimization approaches for analyzing related problems under uncertainty. The research covers both two-stage and multi-stage interdiction models, with emphasis on both modeling and solution methodology development.