Involved in applied research projects, integrating artificial intelligence solutions in industrial environments and developing technologies tailored to specific contexts Contributed to scientific dissemination through article publications and conference presentations on industrial AI applications
HYBLICON Project - In collaboration with MP Ascensores (January 2024 - Present)
- Development of cloud-based AI algorithms to optimize elevator operationthrough learning building usage patterns
- Implementation of machine learning techniques for dynamic elevator behavioadjustment, enhancing user experience and reducing energy consumption
- Technologies:
- Models: Recurrent Neural Networks (RNN, LSTM, GRU) XGBoost
INARI Project - In collaboration with GRI Renewable Industries and Redes (September 2023 - January 2024)
- Research and development of AI models and neural networks for cost predictioof manufacturing complex wind components
- Implementation of intelligent product digital models, experimental projecflow control environments, and advanced cost simulation
- Machine learning models applied to lead time and consumable cosprediction in Seville and Brazil factories
- Technologies:
- Models: Machine Learning models (XGBoost, Random Forestetc) and Deep Learning for tabular data (TabNet, NODE)
- Publication: Results led to the research articlLead-Time Prediction in Wind Tower Manufacturing
OFFSHOREWIND Project - In collaboration with GRI Renewable Industries (March - September 2023)
- Research and development of machine learning algorithms for optimal tassequencing, improving efficiency in wind tower production in the Seville factor
- Technologies:
- Models: Traditional Machine Learning models (decision trees ensemble learning, MLP, among others)
- Award: Recipient of the AEIPRO - IPMA Spain Project Excellence Award in the Change Management/Product Development category
