Jalal Al Rahbani

PhD-trained Bioinformatics Scientist with extensive experience in data analysis and computational biology. Specializing in image segmentation and advanced image processing techniques to analyze complex biological data. Proficient in Python and MATLAB for developing and optimizing computational pipelines and performing statistical analyses. Expertise in leveraging machine learning to classify and analyze large datasets, particularly in the context of cell biology and microscopy. Proven ability to collaborate effectively with interdisciplinary teams to interpret computational results and contribute to groundbreaking research. Strong communicator with experience in presenting complex data insights through reports, presentations, and peer-reviewed publications.

• Charles River Laboratories Fellowship (2021): Awarded based on academic merit for outstanding graduate students in the Department of Physiology, McGill University.
• First Place Poster Presentation at McGill University Departmental Retreat (2018): "Investigation of the role of lysine acetylation in the regulation of the Rac1/Cdc42 regulator CdGAP in cancer."

McGill University Environmental Health and Safety: Introduction to Biosafety, Safe Use of Biological Safety Cabinets, Hazardous Waste Management, Laser Safety Training (2021).

Data Analysis & Bioinformatics

• Expertise in data wrangling, statistical analysis, and data visualization using Python, R, NumPy, Pandas, JMP, and GraphPad.
• Experience in integrating multi-omics data (RNA-seq, proteomics) for comprehensive biological analysis.
• Strong background in bioinformatics tools and techniques, particularly for single-cell analysis and spatial transcriptomics.

Image Processing & Segmentation

• Advanced proficiency in image segmentation using Fiji, ImageJ, OpenCV, and CellProfiler.
• Familiar with tools such as QuPath for large-scale tissue analysis and scikit-image for advanced image processing.
• Experience with machine learning techniques for image classification and feature extraction from microscopy data.

Computational Tools

• Programming languages: Python, R, MATLAB.
• Image analysis software: ImageJ/Fiji, CellProfiler, QuPath.
• Data visualization and statistical tools: GraphPad, Cytoscape, String.
• Experience with cloud computing and high-performance computing environments.

• “CdGAP is a talin-binding protein and a target of TGF-β signaling that promotes HER2-positive breast cancer growth and metastasis” Cell Reports, 2023. doi.org/10.1016/j.celrep.2023.112936
• “Metabolic networks of the human gut microbiota” Microbiology Society Journal, 2020, doi: 10.1099/mic.0.000853