Research in the Risk and Decision Analysis Laboratory is focused on integrating probabilistic risk analysis methods (e.g., dynamic probabilistic risk assessment) with advanced decision-making methods (e.g., reinforcement learning) to improve the safety, security, and efficiency of safety-critical systems, such as nuclear power plants and electric power systems.
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
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
Sponsors
We are grateful to the sponsors of our research!
