We integrate field observations, satellite remote sensing, numerical models, and machine learning to decode the full-depth ocean — from sunlit surface waters to the abyssal dark.
scroll or click arrows belowOur observational backbone spans autonomous underwater vehicles, ship-based CTD surveys, moored buoy arrays, and multi-source satellite remote sensing — capturing sea surface temperature, salinity, currents, and biogeochemical tracers that anchor every model we build.
The continental shelf is where the ocean meets society — driving fisheries, storm surge, and sea-level rise. We study Gulf Stream intrusions, shelf–ocean heat exchange, and biogeochemical cascades, combining high-resolution numerical models with data assimilation.
Working with DSV Alvin and deep-sea profiling floats, we measure thermohaline structure, turbulence, and biogeochemical fluxes from the pycnocline to the abyssal plain — constraining deep-circulation parameterizations and revealing poorly-understood carbon export pathways.
We develop physics-based numerical models fused with machine learning — neural operators, physics-informed networks, and deep-learning emulators — to produce skillful, interpretable ocean forecasts. Multi-platform data assimilation pushes prediction horizons from hours to seasons.