Archives: Team

I am the co-founder of CO2 Angels, a startup helping farmers reduce costs and increase yields through digital soil mapping. My work combines Earth Observation, artificial intelligence, and pedology to enable accurate and scalable soil health monitoring. I hold a Bachelor’s degree in Computer Science (2025) from Babeș-Bolyai University, Cluj-Napoca, and have led CO2 Angels to win RUDEO Hackathon and Innovation Labs Romania, among securing early traction within the first year. At the ESA Φ-lab, I explore next-generation EO modeling, foundation models, and data fusion to advance resilient agriculture.

I am a 3^rd-year PhD candidate in Machine Learning at Imperial College London, based in the Department of Earth Science & Engineering. My research focuses on the “Coupling of Remote Sensing Data and Machine Learning for Greenhouse Gas Emissions Monitoring” and specifically explores generative models (diffusion models), super-resolution, and the analysis of spatio-temporal data for climate applications. Within this context, the collaboration with the European Space Agency (ESA) will investigate the implementation of an “AI-driven, global, near real-time, active fire detection method using remote sensing data”.  The project will aim to improve the accuracy and timeliness of wildfire detection using deep learning techniques explored in the first chapters of my PhD. Prior to my PhD, I obtained a master’s degree in engineering from the Ecole Centrale de Nantes and specialized in Applied Mathematics and Computer Science. When I’m not working, I enjoy running, swimming and discovering new places and people.

Research Activities

Over the 3-month period, the main research areas of the placement will be:

1. Designing and training a super-resolution deep learning model to downscale data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3, using historical data to train the model.
2. Performing anomaly detection with a diffusion model on the downscaled data to identify active fire pixels and newly burnt areas, while also integrating auxiliary physical variables (e.g., vegetation, wind) to ensure that the model’s outputs are physically consistent.

I am interested in modelling and managing complex, non-linear systems—such as energy grids—under environmental stressors. I hold a Geoscientist-in-Training (GIT) license, earned through a BSc in Geoscience (specialist) and Climatology (minor) from the University of Toronto (2023), where I conducted research on the physical impacts of climate change across sectors including energy, agriculture, glaciology, and oceanography.

I completed an MSc in Sustainability, Enterprise and the Environment at the University of Oxford (2024), focusing on the intersection of environmental science, data-driven decision-making, and policy.

Currently, I am pursuing a PhD in Engineering Science at the University of Oxford, affiliated with the Intelligent Earth Centre for Doctoral Training in Artificial Intelligence for the Environment. My research focuses on integrating Observation data with deep reinforcement learning (DRL) techniques to enable adaptive planning and optimisation in dynamic environmental systems. Specifically, I am developing DRL-based control and planning models to improve downstream decision tasks for power grids such as resource allocation, resilience forecasting, and sustainable infrastructure planning. As a visiting researcher, I am eager to explore various ways of leveraging DRL and EO technologies to enhance environmental resilience.

Alice Di Tucci is a postdoctoral researcher at the Center for Quantum Technology and Applications at DESY, Zeuthen. Her research focuses on quantum computing, particularly its near-term applications, including quantum machine learning, combinatorial optimization, and high-energy physics. She is interested in understanding the potential impact of quantum computing in Earth observation applications. She is also involved in teaching and training activities, both within academia and for industry partners, as well as outreach initiatives related to quantum computing.

Alice earned her PhD in quantum cosmology at the Max Planck Institute for Gravitational Physics in Potsdam.

I am Lot van Neerbos, a graduate in Applied Mathematics from TU Delft, specialising in Computational Science and Engineering, with a Bachelor’s degree in Applied Mathematics from the University of Groningen. Fascinated by the intersection of mathematics, Earth sciences and space, I am currently conducting my master’s thesis research at ESA’s Φ-lab. My work focuses on generating high-resolution solar irradiance maps by combining Sentinel-2 imagery and MSG data, using machine learning. I am particularly interested in the potential of remote sensing and data-driven modelling to support sustainable energy applications and climate monitoring.