About me

About

I work at the intersection of research informatics, digital infrastructure, and data science, building systems that make research more scalable, reproducible, and useful. With a foundation in medicine and neuroscience and a career spanning methods development, analysis pipelines, and platform engineering, I bring together scientific domain knowledge and engineering thinking to develop platforms, workflows, and data practices that improve reproducibility and support high-quality research at scale. I’m particularly interested in the space where technical systems, methodological standards, and research impact meet.

Bio

I received my undergraduate degree in medicine (MD) and my PhD in clinical neuroscience from the University of Pécs, Hungary, where I implemented a broad spectrum of in vivo MR techniques in a clinical environment. During my academic career, I studied various several neuroradiological techniques, investigated the assumptions and mechanisms underlying neurofeedback-based and electric neuromodulation, while optimising the experimental setup, protocol and analysis for neuroscientific research. Combining my commitment to clinically applicable research and my keen interest in powerful and reliable methods, I focuse on the development of robust research infrastructure, reproducible and scalable workflows, as well as harmonisation initiatives to improve interoperability for data and methods.

Reproducibility and methodological variation

I am aware of the huge gap between the volume of neuroimaging findings and their translation into mental health research and practice, a gap that can be partially attributed to the lack of reproducibility and confidence in the findings. My primary research interest is to assess and mitigate the effect of methodological variation in research to improve translation potential.

Methods development and Open Science

I am enthusiastic about understanding upcoming methodological improvements in developing end-to-end research infrastructure, supporting data acquisition, processing, and analysis. I lead the initiative of Reproducible Analysis and Statistics, a framework to facilitate reproducible and flexible neuroimaging workflows and allow the assessment and optimisation of the reproducibility of these workflows. I also developed various tools in MATLAB and Python to support multimodal data acquisition (e.g., PyNF, PyNIExp, MLNIExp) and contributed others, including OpenNFT, a Python/Matlab framework for real-time fMRI neurofeedback and GraphVar, user-friendly toolbox for machine learning on functional connectivity measures. I am also a strong supporter of the harmonisation of neuroimaging approaches, and I am involved in international initiatives such as the Brain Imaging Data Structure and Neuroimaging Data Model providing a standard for organising neuroimaging data and analysis.