I am a PhD student at the Data Science and Big Data Laboratory at Pablo de Olavide University. I obtained my bachelor’s degree in Computer Engineering (2021) and my master’s degree in Computer Engineering (2023) at the same university. I have been awarded a grant from the University Teaching Staff Training Program (FPU), which allows me to devote myself full-time to the development of my doctoral thesis.
My work links advanced machine learning with real agricultural problems: I develop software and apply deep learning models to improve forecasting and decision-making in crop management. I am particularly interested in the fusion of different deep learning architectures, combining the strengths of convolutional, recurrent, and transformer-based models, and the integration of multimodal data sources, such as satellite images, meteorological records, and biological measurements. By bringing together these diverse data streams, my goal is to build more robust, reliable, and interpretable systems for predicting pests, diseases, and other agro-environmental phenomena.
A constant theme in my research is tackling the practical problems that arise in real deployments: working with unbalanced datasets common in agriculture, tuning and optimizing hyperparameters for better performance, and improving the interpretability of complex models so results are actionable for end users. I have contributed to tools and applied studies on feature selection and forecasting for agricultural pests, always aiming for solutions that researchers and practitioners can use in the field.
Additionally, I taught on the Degree in Computer Engineering and the Degree in Biotechnology at the University Pablo de Olavide during the academic year 2023–2024 and in the first semester of the 2024–2025 academic year. I remain open to collaborations that connect machine learning methods with real agricultural and environmental challenges, and I welcome contact from researchers, practitioners and stakeholders interested in multimodal learning and applied AI for sustainable agriculture.
Publications
2025 |
A New Transformer-Based Hybrid Model to Forecast Olive Fruit Fly Using Multimodal Data Conference HAIS 20th International Conference on Hybrid Artificial Intelligent Systems, Lecture Notes in Artificial Intelligence 2025. |
A multimodal hybrid deep learning approach for pest forecasting using time series and satellite images Journal Article In: Information Fusion, vol. 124, pp. 103350, 2025. |
A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting Journal Article In: Neural Computing and Applications, 2025. |
2024 |
Explainable Olive grove and Grapevine pest forecasting through machine learning-based classification and regression Journal Article In: Results in Engineering, vol. 24, pp. 103058, 2024. |
2023 |
FS-Studio: An extensive and efficient feature selection experimentation tool for Weka Explorer Journal Article In: SoftwareX, vol. 23, pp. 101401, 2023. |
Olive oil fly population pest forecasting using explainable deep learning Conference SOCO 18th International Conference on Soft Computing Models in Industrial and Environmental Applications , Lecture Notes in Networks and Systems 2023. |
2022 |
HAIS 17th International Conference on Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science 2022. |
2021 |
Earthquake Prediction in California using Feature Selection techniques Conference SOCO 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent Systems and Computing 2021. |