Integrating remote sensing, machine learning, and advanced ocean modeling for the improved prediction of coastal change

The research focuses on the use of computational techniques to develop a better understanding of the coastal environment. This project covers a wide scope and thus allows for flexibility in where to target my research on. Some of the reasons why the prior techniques are specified are due to the possibility of using real-world data such as remote sensing imagery to drive improvements in our models through machine learning techniques.

Some of the current ongoing key projects within my PhD are developing improved bathymetric data covering the Chagos Archipelago and its application for understanding fluid flow through the region which can, in turn, support further research in understanding erosion patterns, the flow of materials/nutrients, etc. Another major concept being explored focuses on the usage of physics-driven machine learning algorithms to allow for a cheap/fast prediction of an expensive flow model for changes/perturbations in the local conditions.