Dan Marrable completed a PhD in Earth observation and remote sensing at Curtin University after graduating with first class honours in applied physics.
Prior to joining the Curtin Institute for Computation (CIC), Dan has worked on numerous projects funded by the European Space Agency and World Bank, which included calibration and validation of Earth observation satellites, shallow water habitat mapping from space and radiative transfer modelling. During this time, he has gained extensive experience in processing very large data sets and coding complex light models on a variety of platforms such as cloud infrastructure, supercomputers and GPUs. Further, he gained knowledge in both field and laboratory-based measurements through many oceanographic field trips to Montgomery Reef, Barrow Island and other coastal field trips around the Kimberly region.
Dan joined the CIC in 2017 and has been working on various projects, including collaborations with fish ecologists from Curtin’s fish ecologist lab and the Australian Institute for Marine Science (AIMS) to automate much of the manual labour required for counting and measuring fish from underwater videos using machine learning.
His key competences and research interests include:
- Machine learning in computer vision
- Radiative transfer modelling
- Data analytics and visualisation