I spearheaded the analysis and development of intelligent models based on audio recordings of patients to predict the presence of persistent COVID. My primary focus was to utilize Python, leveraging tools such as Scikit-learn, PyTorch, and specialized libraries like openSMILE and Librosa to design and effectively prototype both the acoustic representations and the models.Additionally, I developed a service using Flask that consumed data through the FHIR communication protocol via API-REST. This service was crucial for data collection and curation during the training and optimization phases of the models, ensuring secure and efficient processing of information from the patients involved in the study.Throughout this project, I maintained a close collaboration with the Servicio Galego de Saude (SERGAS) and Bahía Software. This strategic collaboration led to successful outcomes in applying artificial intelligence in the healthcare sector. Our efforts culminated in a notable accuracy in predicting patients with persistent COVID, an achievement we documented and shared through a scientific article. This work significantly contributed to the advancement of knowledge in this crucial field.All the code and relevant details of this project are available in the project's GitHub repository, showcasing my commitment to transparency and accessibility to pertinent information.I take pride in being a part of this innovative project and making a substantial contribution to the enhanced diagnosis and treatment of patients with persistent COVID. I am eager to apply this experience and passion to future challenges related to artificial intelligence and healthcare.

I was hired as a Speech Technologies and Artificial Intelligence Specialist, tasked with leading the research and development of state-of-the-art technologies for automatic subtitling in the Galician language.During this experience, my role involved the design and construction of a database using material from various production companies involved in the study. This database was crucial for measuring and comparing the performance (effectiveness and efficiency) of various automatic transcription systems, both commercial and prototypes developed at the University of Vigo. I configured different test scenarios and evaluated the performance of platforms such as Google Cloud and systems like Whisper.The research results concluded that, for the Galician language, the transcription systems developed at the University of Vigo offer the best results. However, it was also emphasized that they are still below the quality level that commercial systems achieve for more widely spoken languages like Spanish or English. These findings provide valuable insights for future advancements in automatic subtitling technology in the Galician language.This experience allowed me to delve deeper into the field of speech technologies and artificial intelligence, providing me with specialized knowledge and a unique perspective on how to apply these technologies in specific linguistic contexts, thus contributing to the advancement of automatic subtitling technologies in the Galician language.

Member of the Galician language team in the project European Language Equality.

PhD Student at the PhD Program from the University of Vigo in the research line of Signal Processing.

Researcher at the Image and Signal group in the DATYS's Research Department.