Research program
Research
How can we produce reliable estimates and predictions for chaotic geophysical systems when models are imperfect and observations are sparse, noisy, and uncertain?
I develop physics-informed and probabilistic machine-learning methods that connect mathematical structure, computation, and observations.
Recovering hidden dynamics from limited observations
Scientific machine learning & data assimilation
I develop methods that use physical structure and learned correction policies to recover unresolved states from sparse or coarse observations. This includes continuous and discrete data assimilation, physics-informed neural surrogates for downscaling, and reinforcement-learning strategies for chaotic systems. Lorenz models and Rayleigh–Bénard convection provide controlled settings for testing learned estimates under limited information.
Treating uncertainty as part of the scientific result
Uncertainty quantification & probabilistic learning
Uncertainty is part of the model, not an afterthought. I use Bayesian neural networks, posterior predictive evaluation, model selection, and inverse methods to represent uncertainty in data, parameters, and predictions. This work asks how probabilistic learning can support calibrated retrievals and more defensible decisions when observations are noisy and several models can plausibly explain them.
Connecting methods to consequential physical systems
Geophysical fluid dynamics & atmosphere–ocean applications
The methodological work is motivated by atmosphere–ocean and climate questions: turbulent fluxes in the atmospheric boundary layer, satellite ocean-color retrieval, mesoscale eddies, and marine-pollution source identification. Across these applications, the goal is to connect mathematical structure, computation, and observations without hiding the uncertainty that shapes the scientific answer.
Continue exploring