About
Discover more about me and my commitment to excellence in education and R&D
Academic Profile
I am an engineer and computer scientist with an academic focus on data management as a foundation for trustworthy artificial intelligence.
My work lies at the intersection of Computer Science, data-intensive systems, and interdisciplinary research on the societal and institutional dimensions of digital technologies.
Rather than approaching trustworthiness in AI as a purely algorithmic challenge, I study how data practices, infrastructures, and governance mechanisms shape the reliability, transparency, and sustainability of AI systems in real-world contexts.
Academic Trajectory
My academic career began in Argentina, where I spent more than ten years as a university professor and researcher in Computer Science, Engineering, and related disciplines.
During this period, I taught undergraduate and engineering courses, supervised numerous theses, led research and development projects, and actively contributed to academic governance and curriculum development.
My research in this phase focused on sensor networks, cloud computing, and data-intensive systems applied to domains such as agriculture, environmental monitoring, and public infrastructure (work that grounded my academic perspective in applied, context-aware computing).
Following this extensive academic experience in Latin America, I continued my career in Europe, where I currently hold senior research and teaching roles in Austria. This international trajectory has allowed me to integrate long-term empirical research experience with European research ecosystems, interdisciplinary collaboration, and large-scale project coordination.
Approach to Data, AI, and Trust
At the core of my academic work is the conviction that trustworthy AI is fundamentally a data management problem.
I investigate how data acquisition, integration, curation, provenance, interoperability, sharing, and governance directly influence the behavior and trustworthiness of AI systems.
From this perspective, artificial intelligence systems are understood as data-driven socio-technical systems, embedded in institutional, industrial, regulatory, environmental, and application-specific contexts.
This approach informs my research on data spaces, FAIR data management, sovereign data processing, and sustainable data processing infrastructures, as well as applied work on data-driven decision support in domains such as agriculture and industry.
Teaching and Academic Values
Teaching is a central component of my academic identity. Shaped by more than 18 years of university teaching in diverse institutional contexts in Argentina and Europe, my teaching philosophy emphasizes the integration of solid technical foundations with critical reflection on the broader implications of data-driven technologies.
In my courses, I aim to equip future computer scientists and engineers with both the technical competencies required to design complex data systems and the ability to critically assess their societal, institutional, and environmental consequences.
My current and former Institutions
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