My research focuses on data management as a foundational pillar of trustworthy artificial intelligence.
I investigate how data acquisition, integration, curation, provenance, interoperability, and governance shape the reliability, transparency, robustness, and sustainability of AI systems. Rather than treating trustworthiness as a purely algorithmic property, my work conceptualizes AI as a data-driven sociotechnical system, where technical design choices are deeply intertwined with institutional, regulatory, environmental, and application-specific contexts.
This perspective is grounded in long-term empirical research on data-intensive systems deployed in real-world settings, including industry, agriculture, and public-sector environments.
Data Management for Trustworthy AI
This research line addresses the data-centric foundations of AI trustworthiness. My work explores how data quality, provenance, lifecycle management, and governance mechanisms directly influence the behavior and accountability of AI systems.
Key topics include:
- Data quality and data lifecycle management for AI pipelines
- Provenance, traceability, and transparency of data-driven decisions
- Trustworthy data processing
- Energy-efficient data pipelines and sustainable AI infrastructures
Data Infrastructures, Data Spaces, and Governance
In this line, I study data infrastructures and data spaces as institutional and organizational frameworks that enable trustworthy data sharing for AI across organizational boundaries. My research examines how architectural choices, interoperability standards, and governance models shape data accessibility, control, and accountability in complex data ecosystems.
Key topics include:
- Data spaces and FAIR data management platforms
- Governance models for cross-organizational data sharing
- Data infrastructures for industry, public administration, and sustainability
- Trust and accountability in distributed data ecosystems
Data-Driven Sustainability and Environmental Risk Management
This research line focuses on the use of data-intensive systems to support sustainability and environmental decision-making. A central theme of this work is frost prediction in agriculture, where I have conducted long-term research combining sensor networks, cloud-based data management, and machine learning models to support risk mitigation in climate-sensitive rural contexts.
Key topics include:
- Sensor networks and data pipelines for environmental monitoring
- Machine learning models supported by robust data infrastructures
- Data-driven decision support for agriculture and environmental risk
- Sustainability assessment of data-intensive systems
