BSc Physical Geography; MSc Oceanography (Physical, Biological and Ecological)

My current project focuses on utilising machine learning methods to gain better understanding of environmental processes. This method offers possible clues on causality of processes, rather than just correlation between parameters. In particular my research will focus on a journey of Saharan mineral dust and its further impact on: displacement of West African Monsoon system, biogeochemical cycles and productivity of central Atlantic Ocean, and biogeochemical cycles and the productivity of the Amazon rainforest. For example, the Amazon rainforest’s soils are found to be nutrient poor, with no clear sources of essential micro- and macronutrients (phosphorous and iron in particular). Current theory of Saharan mineral dust enriching Amazon rainforest soils is fairly well known. However, annual quantity estimates of transported dust (and thus the nutrients) yield large uncertainties and vary between the studies.

The power of AI may provide us with reduced uncertainties, better estimates, and timely predictions. CNN’s ability to analyse dozens of parameters over decades of data sets may unveil new or previously overlooked causalities. The possibility of solving a long-standing scientific question is what inspires and drives me throughout my research.