Laboratories

Research Research in the Risk and Decision Analysis Laboratory is focused on risk analysis and decision analysis for complex industrial systems, for example, nuclear power plants, electric power systems. Particularly, we focus on developing dynamic methods for probabilistic risk assessment that are suitable for modeling the dynamic and complex interactions and dependencies between these different aspects. We use risk analysis to support risk-informed decision-making to cost-effectively improve system reliability and performance through risk-informed design optimization, maintenance policy optimization, cyber attack response optimization, etc. We use existing and develop new methods, models, and algorithms to address these risk analysis and decision analysis problems, for example, dynamic probabilistic risk assessment, game theory, Bayesian analysis, Bayesian networks, cognitive modeling and simulation, dynamic programming, Markov decision process. We are also interested in probabilistic inference problems, for example, cyber attack detection, system health monitoring, the solution to which can support decision analysis. Research areas Risk analysis and reliability engineering Probabilistic risk assessment Dynamic probabilistic risk assessment Human reliability analysis Cybersecurity risk analysis Decision analysis Maintenance policy optimization Cyber attack response optimization Nuclear emergency response Probabilistic inference Fault diagnosis Cyber attack detection Health monitoring Applications Nuclear power plants Electric power systems Methods used and developed Bayesian analysis Bayesian networks Monte Carlo simulation Game theory Particle filtering Dynamic programming Markov decision process (including Partially observable Markov decision process) Neural networks