I am a Postdoctoral Researcher in Pharmacology and Physiology at the Université de Montréal. My work bridges advanced experimental biology with computational data analysis, driven by a commitment to open science, knowledge sharing, and reproducible research.

Here, I share custom code, analysis pipelines, and workflow automation tools to help streamline data processing and support the broader scientific community.

My Philosophy: Knowledge belongs in the open. I believe that thoughtful experimental planning, combined with transparent, robust, and reproducible analytical pipelines, is key to generating reliable scientific insights.

My research specializes in neurovascular coupling, spanning from ex vivo (acute mouse brain slices) to in vivo models.

• Advanced Imaging & Electrophysiology: Extensive experience in contrast, confocal, and two-photon microscopy, combined with electrophysiological recordings.
• Tissue & Cellular Assays: Strong background in immunofluorescence (staining, acquisition, and quantification) as well as Flow Cytometry (FACS).
• Molecular Biology & Genomics: Proficient in genomic and protein workflows, including RNA extraction, RT-PCR, Western Blot, and bulk RNA-seq.

To extract meaningful insights from complex biomedical datasets, I develop custom, automated, and reproducible analytical pipelines:

• Data Analysis & Scripting: Writing efficient scripts in R and Python to ensure rapid, scalable, and reproducible data processing.
• Image Analysis: Automating image processing workflows using Fiji/ImageJ macro scripting.
• Rigorous Statistics: A core focus on applying just, robust, and accurate statistical models to physiological and genomic data.

• Image Processing: Fiji / ImageJ
• Programming Languages: Python, R
• Data Science & Stats: Pandas, NumPy, Scikit-Learn, ggplot2, Tidyverse
• Tools & Environment: VS Code, RStudio, Jupyter Notebooks, Git/GitHub

• Professional: LinkedIn
• Academic: ORCID iD | Google Scholar | Research Gate
• Location: Montréal, QC, Canada