I am a researcher in machine learning, cybersecurity, trustworthy artificial intelligence, and privacy-preserving technologies. My research interests focus on interpretable and trustworthy machine learning, explainable deep learning, anomaly detection, privacy-preserving AI, federated learning security, and applied computational statistics.
Background
I received my PhD in Information and Telecommunications Technologies from the Universidade de Vigo in 2024, awarded with Cum Laude distinction, International Mention, and Industrial Doctorate distinction, with the dissertation entitled “Machine Learning Approaches and Explainability for Real-Time Cyberattack Detection”. During my doctoral studies, I carried out a predoctoral research stay at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome, where I investigated reinforcement-learning approaches for the mitigation of denial-of-service attacks in smart grids.
From 2022 to May 2026, I served as Technical Manager of Data Analytics & AI at the Galician Research and Development Center in Advanced Telecommunications (Gradiant), leading multidisciplinary R&D activities in artificial intelligence, cybersecurity, privacy-enhancing technologies, and data analytics. In Summer 2026, I was selected as Research Fellow in the highly competitive MATS Program. Previously, I worked as Software Engineer at the Microsoft Canada Development Centre within the Core Data Engineering group, developing large-scale telemetry processing solutions for Windows and Azure cloud infrastructures. Earlier in my career, I also worked as research assistant at Universidad Carlos III de Madrid on biometric data-processing and privacy-related research projects. I additionally held a position as Associate Lecturer at the Department of Statistics and Operations Research at the Universidade de Vigo during 2023/2024 and 2025/2026 academic years.
Research
My research focuses on trustworthy and secure artificial intelligence, particularly in the areas of AI safety and security, privacy-preserving machine learning, explainability, and AI-based cybersecurity. I worked extensively on anomaly and cyberattack detection for industrial and critical infrastructures, behavioral analytics (UEBA), privacy attacks and defenses in federated learning, differential privacy, and anonymization methods for sensitive data. My work also explores interpretable and robust machine learning systems, combining modern deep learning approaches with principles from statistical modeling such as Generalized Additive Models to achieve transparency and trustworthy AI.
Open Science
As part of my commitment to open science, I developed and maintained open-source scientific software, most notably the neuralGAM packages for R and Python, implementing interpretable neural generalized additive models. These tools have accumulated more than 250,000 downloads worldwide and are used for interpretable machine learning and statistical modeling applications.
Projects & Funding
During the last years, I’ve participated in 14 competitive European, national, and regional R&D projects, including Horizon Europe, H2020, CDTI Cervera networks, INCIBE, and Red.es initiatives, with a combined budget exceeding 60 million euros. I’ve held different roles, including principal investigator, consortium coordinator, work-package leader, technical lead, and research team member.
Publications & Impact
My scientific contributions include peer-reviewed publications in journals as well as contributions to international conferences including ACM IH&MMSec or IEEE EuroS&PW. In addition, I’ve supervised bachelor, master, and doctoral theses in cybersecurity, privacy-preserving AI, and explainable machine learning, including award-winning student projects.
My complete CV is available in the following links in English and in Spanish.