What are the main topics or questions that drive your research?

My work focuses on building the data and machine learning infrastructure that supports multimodal research within CeTI. I design and maintain scalable pipelines that handle ingestion, synchronization, metadata management, data lineage, and reproducible preprocessing across heterogeneous data sources.

A central part of my work is establishing structured MLOps practices. This includes experiment tracking, dataset and model versioning, containerized environments, automated validation, and continuous integration workflows. The objective is to move from isolated research prototypes toward robust, reproducible, and deployable AI systems.

What inspired you to pursue your current field of work?

I am motivated by the challenge of translating complex research ideas into reliable technical systems. Infrastructure and MLOps sit at the interface between experimentation and production, which makes the work both technically demanding and strategically important.

What excites you most about being part of CeTI?

CeTI brings together diverse disciplines with a shared goal of advancing human centered communication technologies. Being part of this collaborative ecosystem allows me to contribute to infrastructure that enables research across domains.

Which challenge or question has recently sparked your curiosity? 

I am particularly interested in how MLOps frameworks can be adapted for safety critical and real time systems, especially with respect to monitoring, validation, and governance of multimodal models.

How do you like to recharge or spend your time outside of work?

I play piano and saxophone, enjoy long walks, and I am currently learning Greek