I am interested in the use of physics-based AI applied to EO data, in order to better understand the environmental and ecological interactions underlying the Earth system, particularly in light of environmental challenges such as climate change and ecosystem vulnerability. As an Internal Research Fellow at the Φ-lab, my main research focuses on applying AI to emulate physical radiative transfer models (RTMs). I am currently employing this method for Methane retrieval from Sentinel 5P’s (S5P) TROPospheric Monitoring Instrument (TROPOMI), through using AI to learn the physics of costly RTMs within current retrieval schemes. I also work on building internal Φ-lab capacity for deploying AI at the edge, specifically on the Φ-sat-1 experiment to develop a cloud detection algorithm for hyperspectral data on-board a CubeSat, as well as providing support for hyperspectral data processing and parameter retrieval for forestry and agricultural applications. I have a degree in Geography and an MSc in Remote Sensing (MSc) from University College London (UCL), and a PhD from UCL, co-supervised and funded by the European Commission’s Joint Research Centre, on the use of radiative transfer modelling to underpin uncertainty and traceability for EO products.
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