Moiz Sajid is a Senior Data Scientist with 11 years of experience specializing in computer vision and NLP, currently building privacy-aware AI at Securiti from Pakistan. He combines academic rigor—an MSc in Informatics from Technical University Munich and a master's thesis on multiview 3D shape reconstruction at the German Aerospace Center—with production ML engineering roles at companies like Derq. Moiz contributes to open-source synthetic data tooling, notably integrating the Pix3D dataset into the BlenderProc pipeline to enable photorealistic training image generation for 3D-aware models. His background spans research labs, industry startups, and teaching, giving him a rare blend of applied research and deployment experience across perception stacks. Beyond models and code, he focuses on making machines intelligent in practical, data-efficient ways that translate to product impact.
11 years of coding experience
2 years of employment as a software developer
Master of Science, Informatics, Master of Science, Informatics at Technical University Munich
GCE Ordinary Level, GCE Ordinary Level at Saint Mary's Academy
Bachelor of Science (B.S.), Computer Science, 3.73/4.00, Bachelor of Science (B.S.), Computer Science, 3.73/4.00 at National University of Computer and Emerging Sciences
GCE Advanced Level, Physics, Chemistry, Further Mathematics, GCE Advanced Level, Physics, Chemistry, Further Mathematics at Roots School System
EPGY Summer Institutes, Investigations in Engineering - Structural Engineering, EPGY Summer Institutes, Investigations in Engineering - Structural Engineering at Stanford University
A procedural Blender pipeline for photorealistic training image generation
Role in this project:
ML Engineer
Contributions:1 review, 7 commits, 1 PR in 3 days
Contributions summary:Moiz primarily contributed to integrating the Pix3D dataset into the BlenderProc pipeline for synthetic data generation. This involved creating a loader module and a download script to acquire and process 3D object models from the dataset. The modifications included handling material properties within Blender and setting category IDs for segmentation. Further changes involved updating examples and minor adjustments to the download script and dataset handling.
Contributions:3 pushes, 1 branch in 8 years 1 month
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