Francesco Masulli

Papers

Total Citations

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About

Francesco Masulli is a leading researcher at the intersection of artificial intelligence, computer vision, and sustainable manufacturing. His primary research areas include deep learning, robotic automation, and circular economy technologies, with a particular focus on solving real-world industrial challenges. Masulli’s most notable contributions involve developing advanced computer vision systems that enhance automation and resource recovery. His 2024 paper on a deep learning-powered system for selective disassembly of waste printed circuit boards (9 citations) addresses the critical issue of electronic waste, aligning with EU directives on critical raw material circularity. Another key work introduces computer vision algorithms on a Raspberry Pi 4 for automated depalletizing (4 citations), demonstrating cost-effective solutions for detecting and locating variable-shaped objects in industrial settings. Masulli’s research is distinguished by its practical impact, bridging cutting-edge AI with tangible applications in recycling and manufacturing automation. His work not only advances technical knowledge but also contributes to environmental sustainability, making him a valuable voice in the push toward smarter, greener industrial processes.