We are interested in analyzing Large-Scale Complex Systems under Uncertainty using stochastic modeling, Markov Decision Theory and Game Theory with applications to transportation, supply chains, production and manufacturing, and network protection.

In Game Research for Infrastructure SecuriTy (GRIST) LAB, we consider network protection games in which an adversary and a defender compete with each other within the context of a game. The adversary wants to damage the network while the defender wants to thwart the adversary's attack. The primary objective of this project is to devise tools to help government agencies train their staff to overcome uncertain and incomplete information in order to secure densely populated public spaces. We are developing virtual games built inside an immersive virtual world designed to 1) collect data about the players' behavior (defender, adversary, and people), 2) validate our game models using metrics such as the expected damage, the fraction of unsuccessful attacks, etc., and 3) use as a training tool for first responders. You could play the 2-D version of our game at GRIST LAB.

We develop data-driven decision tools to help rail inspectors schedule efficient and timely inspection/maintenance in order to create a more efficient and reliable train network for commuters and travelers.

We investigate reliability measures to assess the performance of roadway traffic subject to non-recurrent incidents, by means of applied queueing theory. We study properties of roadway traffic —such as travel time and traffic density— when the system is subject to random deteriorations of the quality of service.