The ocean as seen from STS-52 in November 1992.

Actionable ocean insight for Earth's climate.

I am a physical oceanographer using computer science/dynamical systems tools to explore ocean dynamics on decadal to climate scales. Passionate about bringing together different branches of oceanography, my goal is to discover the underlying principles that govern ocean dynamics from small to global scales. The focus of my group is on physics, connecting observational and model efforts, but we also work on ocean acidification and ecology in collaborative efforts. We focus on the global ocean, using scalable methods, with a special interest in the Southern Ocean and the North Atlantic.

I lead the Computational Climate and Ocean Group at UC Davis. One current focus of my group, among others listed under Research, is on inferring sub-surface 3D ocean dynamics using surface fields, with the aim of improving our understanding of climate as well as our skill in long range weather forecasts (weeks to months). This work leverages trustworthy (interpretable and explainable) machine learning, and is designed for use with CMIP6 or similar model data that we've used to investigate the Atlantic and Southern Ocean's role in climate and also the global climate mode El Nino Southern Oscillation. This work continues the development of the SAGE (Systematic AGgregated Eco-province) and NEMI (Native Emergent Manifold Interrogation) methods, combining statistical tools, interpretable and explainable machine learning and graphs, designed to work with non-linear data ubiquitous in oceanography and beyond.

Research Areas

Determining emergent global patterns in ocean physics and ecology

The observed and simulated ocean often produces vast quantities of high dimensional data. I use oceanographic theory, methods from numerical analysis and machine learning/data science to expose the key underlying patterns that drive physical and biogeochemical variability. The methods address challenges common across data from the geosciences.

Using deep learning for prediction of physical regimes and biological provinces

Knowing what lies beneath the surface, both figuratively and physically can offer great data analysis and collection strategy. My work uses trustworthy machine learning, combining interpretability and explainability, to give in-depth ocean insight into patterns within climate models using only limited surface fields. Beyond models, I also aim to support extreme event prediction and attribution such as hurricanes and El Nino Southern Oscillation, by tying into and supporting observational frameworks.