Engineer, data scientist and budding limnologist.
I apply satellite remote sensing and machine learning to monitor and forecast cyanobacterial blooms in inland water bodies, and am currently carrying doctoral research around this theme.
From Jan - April 2024 I worked as a remote sensing scientist on the Harmful Algal Bloom Intelligent Twin (HABIT) project in collaboration with 3DEO and the University of Stirling, developing cloud infrastructure for automatically processing and visualising satellite imagery of algal blooms in Lough Neagh.
My PhD is funded by the Hydro Nation Scholars programme.
Article
Atton Beckmann D, Spyrakos E, Hunter P & Jones ID (2025) Widespread phytoplankton monitoring in small lakes: a case study comparing satellite imagery from planet SuperDoves and ESA sentinel-2. Frontiers in Remote Sensing, 6. https://doi.org/10.3389/frsen.2025.1549119
Article
Are more data always better? – Machine learning forecasting of algae based on long-term observations
Atton Beckmann D, Werther M, Mackay EB, Spyrakos E, Hunter P & Jones ID (2025) Are more data always better? – Machine learning forecasting of algae based on long-term observations. Journal of Environmental Management, 373, Art. No.: 123478. https://doi.org/10.1016/j.jenvman.2024.123478
Article
Resolving phytoplankton pigments from spectral images using convolutional neural networks
Salmi P, Pölönen I, Beckmann DA, Calderini ML, May L, Olszewska J, Perozzi L, Pääkkönen S, Taipale S & Hunter P (2024) Resolving phytoplankton pigments from spectral images using convolutional neural networks. Limnology and Oceanography: Methods, 22 (1), pp. 1-13. https://doi.org/10.1002/lom3.10588
I provide teaching support on a wide range of courses in the Biological and Environmental Sciences department. In December 2024 I was awarded associate fellow status by the Higher Education Academy (AFHEA).
