Research Areas

Machine learning for reduced order and multi-fidelity modeling
Development of stochastic reduced order and multi-fidelity models based on novel machine learning methods.
Structural Health Monitoring (SHM)
Development of novel statistical and probabilistic active and passive sensing SHM methods enabled by advanced stochastic models and machine learning approaches.
Intelligent systems with state sensing and awareness capabilities
Development and exploration of novel data-driven stochastic modeling and learning approaches for enabling intelligent self-aware "fly-by-feel" aerial vehicles of the future
Data-driven stochastic modeling and identification
Development of data-driven stochastic modeling and identification methods for time-invariant, time-varying, and nonlinear systems.