Hi! Thank you for taking the time to visit my portfolio webpage. My name is Eidan, I am a recent graduate from McGill University with an Honours Bachelor of Science (B.Sc) degree in Environment and a minor concentration in Japanese. I have been passionate about science, languages, and contributing to the fight against climate change since high school. I hope to utilize my technical and communication skills, as well as my knowledge of natural science and earth systems, to enact tangible, positive change towards a more sustainable future, all while continuing to learn new languages.
To that end, I undertook several research projects of varying focus and application over the course of my undergraduate career. From designing and implementing methodologies to fill gaps in the historical climate data record to developing algorithms capable of detecting and visualizing wildfires, to producing a comprehensive review of an official Environmental Impact Assessment report for an Albertan formaldehyde plant, and more. I even gave presentations and wrote papers in Japanese regarding environmental issues I am passionate about in an effort to build my cross-cultural science communication skills. A repository containing many of these works and my contributions can be found under the Publications, Projects & Presentations page of this website.
In the last few years of my undergraduate experience, I took an interest in applications of remote sensing for natural resource management and helped to conduct research in Dr. Jeffrey Cardille's (McGill Department of Natural Resource Sciences) Computational Landscape Ecology Laboratory. In order to bolster my GIS skills, I took Dr. Cardille's ENVB 530 (Advanced GIS for Natural Resource Management) course in Winter 2022. This portfolio contains many of the deliverables required for the course, each of which was designed to improve the students' skills and broaden their experience with a number of GIS applications and software, ranging from an in-depth exploration of Model Building with Python in ArcGIS 10.8.x to a crash-course in the environmental applications of Google Earth Engine (GEE).
My Final Project for this course takes a deeper look at my research with Dr. Cardille where I theorize and develop improvements to the wildfire burn scar detection capabilities of BULC-D – a GEE-based prototype of Dr. Cardille's Bayesian Updating of Land Cover (BULC) algorithm that employs methods such as harmonic time series modeling, bayesian statistics, and Z-scoring. This work was later expanded and improved upon in my culminating undergraduate Honours Thesis, Cross-comparing Burn Indices for Improved Visualization of Wildfire Burn Scars: Bayesian Updating of Land Cover (BULC-D).