I am a research software engineer at the Paul Scherrer Institute (PSI, Switzerland) working on IT solutions for open science, particularly for applications involving interoperability of open research data (ORD) across experimental/simulation platforms as part of a growing effort towards fully autonomous laboratories to accelerate materials discovery.

My career has evolved from management and logistics to research, software development (in both academia and industry), and now scientific project management and open science IT solutions design. I am passionate about clean, efficient solutions, intuitive UI/UX, hardware/software integration, lab automation, and collaborative innovation. Academic research honed my analytical problem-solving skills in a flexible environment, while my industry experience in IT solutions for telecommunications reinforced them in product-driven, team-based settings with stricter timelines.

Currently, I focus on advancing scientific and IT solutions for the open science community, emphasizing open research data integration and interoperability across experimental and computational systems. I am a member of the Swiss ORD PREMISE project, working to establish standards and protocols for seamless data exchange between experiment and simulation platforms. Additionally, I co-organize the PREMISE-sponsored MADICES workshop series, bringing together ontology experts, open science platform developers, and researchers to enhance research interoperability.

I contribute to the development of the AiiDA workflow management system, its graphical interface (AiiDAlab), and the Materials Cloud open research web platform. I am also involved in automating experimental labs and designing experiment/simulation workflows to accelerate materials discovery (see for example my work on the integration of AiiDA with the Aurora battery testing platform at Empa, Switzerland).

My goal is to lead a team of scientific IT specialists in designing cutting-edge interfaces that integrate hardware and software, streamline experimental and computational workflows, and leverage robotics, AI, ML, and HPC to enable fully autonomous laboratories.