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 CIC, Dan 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 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 regarding both field and laboratory-based measurements from many oceanographic research trips to Montgomery Reef, Barrow Island and other coastal locations in the Kimberly region.
Dan joined the CIC in 2017 and has been working on various projects, including a collaboration with fish ecologists from Curtin’s fish ecology lab and the Australian Institute for Marine Science, using machine learning to automate much of the manual labour required for counting and measuring fish from underwater videos.
His key competencies and research interests include:
- Machine Learning in Computer Vision
- Radiative Transfer Modelling
- Data Analytics and Visualisation