Machine Learning applications in Multidimensional Vulnerability Indices

During the summer of 2024, Andrew worked to develop machine learning models to predict the Multidimensional Vulnerability Index scores for climate-vulnerable countries, based on the indices proposed by both the Caribbean Development Bank and the United Nations. The work also focused on the enhancement of the methodologies used to calculate these vulnerability scores. He presented his results on the predictive models he developed at the 3rd annual Canada Caribbean Institute Research Symposium and at IEEE ICTMOD 2024, the latter for which he has also submitted a conference proceeding that is accepted pending minor revisions. He aims to further collaborate with TTLab to work towards a more data-driven and holistic climate vulnerability index.

Andrew and his fellow researchers at the CCI Research Symposium

Andrew graduated from his Bachelor of Science in Honours Biology at McGill University in the spring of 2024. During the following summer, he was a Queen Elizabeth Scholar and was able to work with TTLab in Trinidad. His research used datasets gathered from the Caribbean Development Bank and the United Nation to apply machine learning methods to improve the methodology behind their respective Multidimensional Vulnerability Indices, with the goal to improve the quantification of a country’s vulnerability to climate change. He has also worked on research projects on evolutionary morphology, at-risk plant species, peat soil, and biochar applications. He plans to pursue graduate studies focusing on the intersection of climate change and invasive species ecology.

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