My background is in biophysics and quantitative imaging, where I worked on extracting meaningful signals from biological experiments using computational approaches. Over time I became increasingly interested in the engineering side of science: building reliable pipelines, automating analysis workflows, and turning research methods into reproducible software.

Today I enjoy working at the intersection of:

🔬 Science
📊 Data
🧠 AI
🛠 Engineering

• Scientific Computing Tools – Tools for data extraction and modeling.
• Image Analysis & Computer Vision – High-throughput microscopy and biological imaging pipelines.
• Data Processing Workflows – Robust, scalable research data pipelines.
• AI-Assisted Research Tools – Leveraging ML to accelerate discovery.
• Developer Utilities – Building for reproducible and open science.

Languages & Core: Python · NumPy · SciPy · OpenCV · scikit-image
Machine Learning: PyTorch · scikit-learn · Deep Learning
Engineering: Git · CI/CD · Docker · Scientific computing & Automation

• Computational Imaging – Pushing the limits of what we can see.
• AI for Scientific Discovery – Applying modern AI to complex biological problems.
• Research Tooling – Bridging the gap between code and experimental science.
• Automation – Making scientific workflows more reliable and reproducible.

I enjoy turning ideas into working tools.
Especially when those tools help researchers explore complex datasets and accelerate discovery.

📍 Strasbourg, France