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If you are interested in any of the projects below at the University of Melbourne, please contact me via email. Please include a writing sample and Generative AI statement with your email.
Gaussian Mixture Modelling-based clusters obtained from the temperature and salinity distribution in the Antarctic shelf region, from the ACCESS-OM2-01 ocean model.
Reconstructing Ocean Salinity with Neural Networks
Reconstructing Ocean Salinity with Neural Networks
In this project, we will explore the usage of machine learning methods in reconstructing missing ocean salinity, using building a gridded ocean dataset, and explore the impact of this missing salinity reconstruction on ocean dynamics.
Snapshot of sea surface temperature in a global 1/6th-degree NumericalEarth.jl ocean.
Building a Hybrid ML-ocean model
Building a Hybrid ML-ocean model
I am a developer of the Oceananigans.jl and NumericalEarth.jl ocean models. These models are GPU-powered, promising up to 50x speed up from traditional CPU-powered ocean models.
In this project, we will explore the possibility of implementing machine learning-powered parameterisations of subgrid-scale processes into the NumericalEarth model. In conjunction with the Department of Mechanical Engineering at University of Melbourne.
Long time-scale Antarctic Bottom Water change
Long time-scale Antarctic Bottom Water change
Armed with the GPU-powered NumericalEarth model, we we will explore the long-term changes in the global overturning circulation in a coarse-resolution model.
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