Bachelor of Science (Hons) in Physical Geography 1st, Kingston University London
Thesis title: Quantifying magnitude of electrostatic forces on pollen grains in wind pollinated Plantago lanceolata. (93%)
Masters of Science in Oceanography (Physical & Biological) with Merits, University of Southampton
Thesis title: Meteorological controls on primary production in stratifying shelf seas of the global oceans. (70%)
1st place, Guy Robinson Dissertation Prize for highest departmental mark, 2018
1st place, Pete Guest Memorial Prize for highest achievements in Ecology and Conservation, 2018
PhD in Environmental Intelligence: AI and Data Science for Sustainable Futures (UKRI CDT studentship)
Thesis title: Wandering Dusts. Using mixed AI methods to predict atmospheric dust transport, and disentangle curious reasons for its impact on the environment.
My current project focuses on utilising mixed AI methods to gain better understanding of environmental processes. Advanced AI methods offer possibility to predict the transport of atmospheric particles based on observational data only. Such methods offer significant improvement in time and accuracy of predictions as compared to traditional, physics-based models. In particular, my research focuses on building a convolutional neural network model capable of predicting the transport of Saharan mineral dust 3-5 days in advance. The second part of research will utilise this model to analyse the impact of Saharan dust on biogeochemical cycles and productivity of the 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. AI'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.
Lead: Dr Benno Simmons - Research Fellow and Lecturer in Ecological Data Science
Data & Methods: Dr Stefan Siegert - Lecturer in Advanced Statistical Modelling and Applied Data Science
Second: Prof Andy Augousti - Professor of Applied Physics and Instrumentation, Kingston University London