Ibrahim Sobh
Papers
2
Total Citations
4
H-Index
2
About
Ibrahim Sobh’s research sits at the exciting intersection of autonomous systems, computer vision, and energy-efficient edge computing. He is best known for his work on perception systems for self-driving vehicles, where he has tackled the challenging problem of fisheye image compression and perception. His comprehensive 2025 study on this topic provides critical insights into how 360-degree near-field vision data can be efficiently processed and transmitted without sacrificing the accuracy needed for safe autonomous driving. In parallel, Sobh has made significant contributions to the hardware side of AI, developing novel Edge AI architectures. His work on power-efficient, re-configurable LP-MAC (Low-Power Multiply-Accumulate) processing elements directly addresses the core challenge of deploying deep learning models on resource-constrained edge devices. By proposing hardware that balances computational power with a minimal energy and area footprint, his research helps bridge the gap between advanced AI algorithms and practical, real-world deployment in robotics and embedded systems. With his work spanning both algorithmic perception and efficient hardware design, Sobh is a key figure in making intelligent, autonomous systems more practical and accessible.
Research Focus
Key Achievements
Top Papers
- 1
- 2