Mazen Abdelfattah

University of British Columbia, Qatar University

Papers

Total Citations

H-Index

About

Mazen Abdelfattah is a researcher at the forefront of robotic perception and embedded vision systems, with a focus on fusing LiDAR and camera data for autonomous navigation. His most cited work introduces **LiCaS3**, a novel self-supervised method for temporal synchronization between LiDAR and camera sensors. This contribution is critical for reliable 3D perception in robotics, as it eliminates the need for manual calibration—a persistent bottleneck in real-world deployments. By enabling robust sensor fusion without external supervision, Abdelfattah’s approach directly enhances the accuracy and autonomy of perception pipelines in dynamic environments. Beyond perception, he has also contributed to the hardware side of intelligent systems, developing an **IoT reconfigurable System-on-Chip (SoC) platform** tailored for computer vision applications. This work supports the growing demands of Industry 4.0 by integrating smart sensor processing directly into edge devices. While his citation counts are still building—reflecting the early stage of a promising career—his research addresses foundational challenges in sensor synchronization and embedded intelligence. Abdelfattah’s work is particularly relevant for students and engineers working at the intersection of robotics, deep learning, and hardware-software co-design.