The project proposes an in-depth exploration of advanced machine learning techniques for the automated classification of stellar spectra, aiming to significantly reduce computation time compared to current methods used by Astro+. Beyond improving efficiency, the project envisions the development of models capable of directly determining stellar parameters through deep learning, eliminating the need for manual model fitting. This approach would represent a major step toward fully automated and more efficient spectroscopic analysis.

It is intended to develop a set of tools, capable of running on the Unitree Go2 robot, that include mapping, autonomous navigation, and object and person recognition. The developed software will feature a simple interface so that non-expert personnel can use it.

The project aims to create a digital twin that models the cognitive state of older adults to improve their well-being through applications for early detection, monitoring, and personalized assistance against cognitive decline.

Our hypothesis is that an assistance system for people with some kind of disability or phobia must be able to adapt to the particularities of each individual. Until now, most of the systems focused on these people did not allow this adaptation. To test our system we will use two scenarios: people with autism spectrum disorder (ASD) and people with phobias. In addition to the fact that the system will have to be able to adapt to each group, since their characteristics are different, it will also have to adapt to each individual. For example, within ASD there are different degrees and within each degree each individual may have different needs. It should be noted that there are many different types of phobias and in this project we will focus on specific social phobias (phobias generated in specific social situations, such as public speaking (glossophobia), working with a work group, sharing a working lunch, etc.).