Research

Research Focus

Research Interest

My broad academic and research experience can be categorized under the following general headlines. My research interests, however, are not limited to the topics listed below. Rather, they extend to whatever exciting engineering or scientific problems pop up to draw my attention, passion, and curiosity. Consequently, the following list is ever-expanding as I continue working in a research-driven environment.

Wildfire engineering: science and technology development to inform decision-making
Structural health monitoring.
Structural system identification (linear and nonlinear methods).
Stochastic filtering, nonlinear estimation, and nonlinear system identification.
Bayesian inference, Bayesian model updating and model inversion.
Integration of computational models with data for estimation, identification, and data assimilation.
Digital Twinning for diagnosis, prognosis, and decision making
Risk-informed decision making.
Statistical signal processing.
Nonlinear computational solid and structural mechanics, nonlinear finite element response simulation.
Nonlinear mechanics of reinforced concrete.
Analysis, design, and behavior study of structural components and systems.
Large-scale experimental testing.
Structural dynamic and Earthquake engineering.

Research Vision

Lead interdisciplinary research efforts to develop methods and technologies for assimilating, processing, and packaging actionable information and associated uncertainty metrics intended for emergency responders and decision makers in pre-disaster mitigation and post-disaster response and recovery efforts of civil infrastructures and systems.

Conduct fundamental research and develop technological solutions to minimize the societal and economic consequences of disasters. Provide a meaningful contribution to local and nation-wide resilience plans. Enhance public awareness about disaster resilience to draw political attention and improve public policy.

Leverage existing expertise in sensor technology, information technology, Machine Learning and Artificial Intelligence, computer science, and social economy to lead an interdisciplinary and multi-institutional research effort.

Develop methods and techniques to assimilate useful information by infusing physics-based model predictions with measurement data in order to estimate risk, reliability, and resilience metrics at different scales, from structural level to community level.