Al Liang, ’21, Computer Science
I used Southern Ocean State Estimate biogeochemical data and machine learning methods to determine whether biogeochemical data is significant in modeling Antarctic krill distribution. I also investigated the environmental factors that are most critical in predicting krill distribution. I used the machine learning techniques of random forest and boosted regression trees and found that both methods reinforced my findings. During my research, I learned to code in R, a valuable skill for data analysis that I will certainly use in the future. I also created machine learning models and used them to make observations about big data. This internship really increased my interest in using machine learning methods to analyze data, something I didn’t have previous experience doing. I’m looking forward to continuing my research on this topic and seeing what else I can uncover.