About Me

I’m a PhD candidate at the Image Processing Laboratory (IPL) at the Universitat de València. My research lies at the intersection of causal machine learning, climate systems, and development economics—specifically, understanding how climate variability, conflict, and market dynamics shape food security outcomes in Africa.

I hold a Bachelor's degree in Physics from the Universitat de València, where I explored the fundamentals of wave phenomena in my undergraduate thesis on spatial coherence and polarization. During this time, I also studied abroad at the Università degli Studi di Torino (Italy) through the Erasmus program. Later, I pursued a Master’s in Data Science at the Universitat de València. I focused on deep learning methods for atmospheric correction of satellite imagery, applying machine learning to environmental data from space.

Research Projects

I am currently involved in the ThinkingEarth project, funded by the European Union's Horizon Europe program. ThinkingEarth aims to develop the first Copernicus Foundation Models by leveraging advanced AI techniques, including self-supervised learning, graph neural networks, and physics-aware machine learning. The project treats Earth as a complex, interconnected system, creating a data-driven graph representation to model Earth system variables. These models are applied to use cases with high socio-environmental impact, such as accelerating Europe's clean energy transition, understanding Earth's processes through causal teleconnections, and assessing the impact of climate emergencies on biodiversity and food security. More about ThinkingEarth.

Previously, I participated in the Causal4Africa project under the Microsoft Climate Research Initiative. This project focused on investigating food security in Africa through causal inference methodologies. By applying causal discovery and effect estimation from observational data, the project aimed to understand the impact of humanitarian interventions on food security. The insights gained were intended to enhance the usefulness of causal machine learning approaches for climate risk assessment, enabling better interpretation and evaluation of potential interventions. More about Causal4Africa.

I also contributed to the DeepCube project, funded by the European Union's Horizon 2020 program. DeepCube aimed to unlock the potential of Copernicus Earth Observation data by integrating advanced AI and semantic web technologies. The project developed explainable AI pipelines and hybrid modeling approaches that respect physical laws, enhancing our understanding of Earth's processes related to climate change. More about DeepCube.

Additionally, I was involved in the SCALE project, supported by Fundación BBVA. SCALE focused on advancing causal inference in the context of human-biosphere interactions. The project aimed to develop new algorithms capable of understanding complex problems and automatically identifying causal relationships within coupled human-environment systems. More about SCALE.