Uncertainty and extreme events
Rare and intermittent behavior in nonlinear dynamical systems, with an emphasis on mechanisms, statistics, prediction, and mitigation.
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Research
We develop mathematical and computational methods for systems where uncertainty is not peripheral—it determines what can be observed, predicted, and controlled.
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Themes
Scientific machine learning
Data-driven models for dynamical systems that retain physical structure, uncertainty, and interpretable behavior.
Inference and experimental design
Bayesian computation, data assimilation, and adaptive sampling for making decisions in complex engineering systems.
Environmental and geophysical systems
Statistical prediction, state estimation, and reduced modeling for transport, turbulence, and renewable-energy applications.
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Applications
Combustion and energy
Instability detection, statistical modeling, and data-driven control for methane and hydrogen combustion.
Turbulent transport
Intermittency, data assimilation, and statistical prediction in environmental and geophysical flows.
Engineering decisions
Adaptive sensing, surrogate modeling, and optimal design for expensive high-dimensional systems.