Aitsam Muhammad

Designed and executed human-robot interaction experiments using neuromorphic computing, spiking neural networks, and event-driven sensors to enhance robotic perception and responsiveness. Developed real-time computer vision pipelines for object detection, gesture recognition, and adaptive tracking with Python, ROS, OpenCV, TensorFlow, and PyTorch. Engineered a multimodal cognitive load estimation framework combining physiological and behavioral signals to optimize adaptive HRI algorithms. Integrated event-based cameras (DVS) to reduce latency in autonomous decision-making. Authored 6+ peer-reviewed publications and supported cross-lab reproducibility across European Horizon 2020 collaborations.

Designed and executed human-robot interaction experiments using neuromorphic computing, spiking neural networks, and event-driven sensors; developed real-time computer vision pipelines for object detection, gesture recognition, and adaptive tracking; engineered a multimodal cognitive load estimation framework to optimize adaptive HRI algorithms; integrated event-based cameras (DVS) to reduce latency; authored 6+ peer-reviewed publications and contributed to cross-lab reproducibility under Horizon 2020.

Conducted privacy-preserving AI research in connected vehicle systems, applying differential privacy to support GDPR compliance. Analyzed driver behavior patterns and developed AI frameworks for eco-driving with real-time data processing and behavioral analytics. Collaborated with cross-functional teams to deliver technical reports influencing R&D strategies and AI implementation for automotive applications.