Dr Milto Miltiadou (She/her/hers)

Dr Milto Miltiadou is a multi-disciplinary Lecturer (E&R) in Computer Science at University of Exeter, UK. She is an Academic Lead of the Centre for Environmental Intelligence and is affiliated with the Institute of Data Science and Artificial Intelligence (IDSAI). Her research group focuses on forest monitoring; unlocking the potential of Earth Observation data, including time-series and LiDAR, optimising processing pipelines, implementing tools and machine learning applications. She has implemented two open-source tools: PlotToSat (https://github.com/Art-n-MathS/PlotToSat), developed at the University of Cambridge, and DASOS (https://github.com/Art-n-MathS/DASOS), developed at Plymouth Marine Laboratory and University of Bath. She is the supervisor of the MSCA Fellowship ForestFireAI (£260,348EUR), a PhD candidate and an MSc Research student. She was the Co-Investigator of GreenSight project (£89,459.59) completed in collaboration with MetOffice. Before that, at the Cyprus University of Technology, she initiated and managed the ASTARTE and FOREST projects worth €400,000. She co-supervised a PhD student to completion; the joint paper with Dr Rorai Pereira Martins-Neto was selected at Editor's Choice Article in 2023 Series, Forests journal. She co-chairs the Machine Learning for Earth Observation (ML4EO) conference, now preparing for the 5th conference in a row, and serves as the Module Leader of the Programming with Python module. Her experience spans in both academia and industrial innovation, including past international placements at Planetek Italia, Italy, and Interpine Group Ltd, New Zealand, with an ongoing collaboration with the latter.

Research Interests:

• Creating and exploiting time-series of multi-source Earth Observation (EO) data
• Optimising and advancing algorithms for processing and handling large-scale EO and LiDAR datasets
• Generating feature vectors from EO and LiDAR data for use in machine learning models
• Machine learning applications and advancements for forest monitoring
• 3D structural analysis and harmonisation of LiDAR data
• Phenology of multi-source EO data with particular interest in Synthetic Aperture Radar (SAR)