Mohammad Al-Qaderi

Simon Fraser University, Hashemite University

Papers

Total Citations

H-Index

About

Mohammad Al-Qaderi is a leading researcher at the intersection of social robotics, artificial intelligence, and cognitive neuroscience. His work focuses on endowing robots with human-like perceptual abilities, specifically through brain-inspired multi-modal systems that integrate vision and audition. Al-Qaderi’s most significant contribution is the development of a novel perceptual architecture that uses spiking neural networks to fuse face, body, and voice data, enabling social robots to recognize individuals in dynamic, real-world interactions. This work, published in 2018, has garnered 19 citations and is considered foundational in the field of human-robot interaction. He further advanced speaker identification with a two-level system that fuses heterogeneous classifiers and complementary features, achieving robust performance even with limited training data and short utterances—a common challenge in social robotics. With over 49 citations across his top papers, Al-Qaderi’s research is highly influential, bridging the gap between biological neural processing and practical robotic perception. His context-independent, brain-inspired approach has broad applications, from assistive robotics to interactive AI systems, making him a key figure in the quest for more natural and intuitive human-robot collaboration.