Mujadded Alif is a computer vision research engineer and founder with 10 years' experience building production-grade detection and inspection systems, currently focusing on railway and industrial safety at the University of Huddersfield. He designs hybrid real+synthetic datasets and diffusion-based data generation pipelines to overcome class imbalance and environmental noise, and routinely implements and optimizes YOLO variants for low-latency edge deployment on NVIDIA Jetson devices. Mujadded combines hands-on research—publishing on vehicle detection and synthetic hazard generation—with applied engineering practices from his Principal/CTO background to deliver scalable, testable ML systems. He also built VisionHowl, a DataOps platform to accelerate annotation-to-training workflows, and has a track record of improving real-world metrics (e.g., bolt classification from 80% to 93%).
10 years of coding experience
2 years of employment as a software developer
Master's degree, Artificial Intelligence, Master's degree, Artificial Intelligence at The University of Huddersfield
Bachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at BRAC University
Certificate in Full Stack Web devlopment, Computer Software Engineering, Certificate in Full Stack Web devlopment, Computer Software Engineering at freeCodeCamp
Full stack web developer, Web/Multimedia Management and Webmaster, Full stack web developer, Web/Multimedia Management and Webmaster at LICT Web Development Training
Higher Secondary Certificate, Science, Higher Secondary Certificate, Science at Saint Joseph College
Contributions:1 push, 1 branch, 1 comment in 5 years 5 months
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Mujadded Alif - Research Associate In Computer Vision