Diarmuid CorrPhD student
Environmental Science PhD student aspiring to solve the challenges society faces in mitigating climate change. My research focuses on assessing supraglacial hydrology in Antarctica and Greenland. I use machine learning algorithms and optical satellite imagery to map water features on the surface of Earth's ice sheets.
I am a PhD student at the Lancaster Environment Centre. My supervisors are Amber Leeson, Mal McMillan, and Ce Zhang. My research focuses on supraglacial hydrology, specifically the formation of lakes and channels on the surface of ice sheets or glaciers in Antarctica and Greenland. I am interested in the mapping methods used to identify these bodies of water. Throughout my PhD, I have gained experience in using machine learning and remote sensing to identify, map, and monitor melt-water bodies that form in topographic depressions on the ice surface.
I explore various machine learning algorithms such as Neural Networks, Random Forest, Gaussian Processes, and Bayesian Inference. In addition, I have developed my data analysis skills, specifically in processing large data sets and presenting results in a simplified manner. My research has progressed from using pixel-based classification algorithms to using Bayesian inference to describe the spatial variation of the data, which also provides a better approximation of uncertainties than other models.