ML Researcher and Professor
I explore how Artificial Intelligence can help
people/organizations make better decisions

Continuation of the tunnel fan monitoring project initiated in 2022 (FUO-231-22). In this phase, we are focusing on improving anomaly detection, not only independently but also in pairs of fans, as well as optimising fan operation.

Team member

The multidisciplinary ATLAS project was proposed under the CHIST-ERA Call 2023. ATLAS aims to enhance the expressiveness and quality of Geographic Information Systems (GIS) by integrating data from multiple external sources of diverse nature and quality, with a case study focused on urban mapping and flooding. The project contributes to the broader effort of developing and applying tools and methods based on AI, ML and statistics in the water sciences.

Team member

The use of incomplete, inaccurate or partial data in machine learning can lead to biased or inefficient decisions. Commonly, large data sets are used, selecting informative elements and discarding those that do not meet a minimum quality level. Our research group proposes an alternative approach for scenarios where data scarcity requires maximising every piece of available information, a frequent challenge in areas such as industry, finance, economics, and medicine.

Team member

This project is dedicated to developing new methods and applications to address ML challenges in environments characterized by sparse information. It gives priority to the use of low quality data, which is often overlooked. In alignment with Green AI principles, our approach includes designing methodologies that enhance computational efficiency and, as a result, reduce energy consumption. Furthermore, we are adapting these methodologies to address specific industrial challenges, focusing on sustainable practices such as extending the lifespan of equipment and efficiently managing resources like water and energy.

Team member

Working on CITIES DATALEX®, a software whose purpose is to improve access to legal regulations resulting from the application in actions in the urban environment and, in general, in the processes of sustainable urban and territorial development. Also, supervising the IT team in helping gather and process data for the main purpose of the project.

Team member

Project led by my colleague and friend David Anseán. We are working on training machine learning models with simulated data from battery digital twins such as 'Alawa, and adapt them to real data to estimate the health status and remaining life of lithium-ion batteries.

Collaborating researcher