I am a multidisciplinary biomedical data scientist with a Ph.D. in Medical Sciences and over six years of postdoctoral experience at EPFL's Blue Brain Project, where I specialized in computational neuroscience and neuro-glia-vasculature modeling. My research background spans metabolomics, clinical research, and bioinformatics, with a proven track record in developing reproducible workflows, FAIR-compliant data pipelines, and integrative computational models for complex biomedical datasets.
Currently, I serve as a Senior Biomedical Data Scientist (FAIR Data and Open Science) in the Data Science for Biomedical Research Unit (DSBU) at the University of Lausanne (UNIL), where I support large-scale data integration and research data management across life sciences and clinical projects. I am passionate about building tools that make scientific data more accessible and reusable—including developing open-source Python packages for biomedical metadata extraction and standardization.
My expertise bridges clinical research, computational biology, and software development, with proficiency in Python, and version control systems. I am particularly interested in creating solutions that help researchers adhere to FAIR principles, enhance reproducibility, and accelerate discovery through better data management practices.
For the full list of skills and experience, see or download the CV.
How do brain cells control blood supply? This study reveals how astrocytes—the brain's support cells—regulate blood flow through localized vessel diameter changes. Our computational model shows capillaries as key players in cerebral perfusion, with layer-specific responses across cortical depths.
[Battini et al., Biomedicines, 2025]
Identified metabolic biomarkers to predict survival in pancreatic cancer patients using HRMAS NMR spectroscopy. Analyzed 106 tumor samples and discovered that ethanolamine levels distinguish long-term from short-term survivors—a potential clinical biomarker obtainable in 20 minutes during surgery. This publication demonstrates how metabolomics profiling can guide treatment decisions.
[Battini et al., BMC Medicine, 2017]
Distinguished single-gland from multiglandular disease in primary hyperparathyroidism using HRMAS NMR metabolomics. Analyzed 43 samples from 32 patients, identifying a 9-metabolite signature (including phosphorylcholine, choline, and glucose) that accurately differentiates disease subtypes. Applied network analysis with the ADEMA algorithm for metabolite prediction, offering potential for intraoperative surgical guidance.
[Battini et al., Surgery, 2016]
A Python library for multiscale neuro–glia–vascular blood flow modelling, developed at EPFL’s Blue Brain Project. Focus on graph-based vasculature representations, flow simulations, and integration with existing neuroscience tools.
View on GitHubAutomated Snakemake-based pipeline for harmonizing clinical and research metadata across multiple data sources. Implements FAIR principles for improved data interoperability and reusability in biomedical research.
In DevelopmentComprehensive toolkit for validating, extracting, and packaging clinical research data for long-term archiving. Ensures compliance with institutional data management standards and facilitates reproducible research workflows.
In DevelopmentAdvanced graph-based analysis tools for modeling cerebrovascular blood flow dynamics. Combines time-series analysis with machine learning approaches to study neuro-vascular coupling mechanisms.
In DevelopmentPython (NumPy, pandas, SciPy, scikit-learn, PyTorch, Matplotlib, Seaborn), R, Bash, LaTeX. Reproducible workflows with Snakemake, containers (Docker, Singularity), Git/GitHub/GitLab.
FAIR data principles, metadata standards, data stewardship, REDCap, clinical and research data registries, Agile project management (Scrum, Kanban), JIRA, Confluence, Trello, Slack.
For the full list of publications, see Google Scholar.
For collaborations or opportunities in biomedical data science, FAIR data stewardship, or digital health research, feel free to reach out.
stephaniebattini@gmail.com
Google Scholar
LinkedIn profile