Youssef Zidan is a software engineer with a decade of experience building data-driven systems, computer vision pipelines, and ML solutions across research labs, startups, and product teams. He has applied his expertise at the German Aerospace Center developing efficient RGB-D segmentation models for edge devices and contributed backend improvements to the popular BlenderProc project for photorealistic synthetic data generation. His work spans end-to-end stacks—from automated web scrapers, streaming and search integrations (Kafka, Elasticsearch), and NodeJS/Laravel backends to deploying anomaly-detection analytics prototypes—reflecting strong systems and production engineering skills. Currently based in Cairo and pursuing a master’s at TUM, he blends academic rigor with product-minded engineering and a knack for practical dataset synthesis and post-processing techniques that accelerate ML training. An unassuming hoodie enthusiast, he pairs hands-on coding with research-grade experimentation to move vision and ML projects from simulation into deployable reality.
10 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS, Computer science and engineering, A-, Bachelor of Science - BS, Computer science and engineering, A- at The German University in Cairo
A1, German language, A1, German language at ÄDK- Ägyptisch-Deutsches Kulturzentrum
Master of Science - MS, Informatiks, Master of Science - MS, Informatiks at Technical University Munich
Highschool GCSE, Highschool GCSE at Portsaid school
A procedural Blender pipeline for photorealistic training image generation
Role in this project:
Backend Developer
Contributions:134 commits, 1 comment in 10 months
Contributions summary:Youssef primarily worked on implementing and modifying the core rendering pipeline for segmentation maps within the BlenderProc project. They refactored and improved the `SegMapRenderer`, `SuncgLoader`, and `StereoGlobalMatchingWriter` modules, focusing on the integration of new features like multi-view stereoscopic rendering and depth map generation. The user's contributions involve adjusting the image settings, color scaling and post-processing techniques to process rendered outputs.
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