Dillam Jossue Díaz‐Romero

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About

Dillam Jossue Díaz‐Romero is a leading researcher at the intersection of artificial intelligence, circular economy, and sustainable manufacturing. His work focuses on revolutionizing waste management and recycling through advanced computational methods, particularly in the domain of electronic waste (WEEE) and metal scrap sorting. Díaz‐Romero’s major contributions include pioneering deep learning and unsupervised learning techniques for material recovery. His highly cited paper, “Simultaneous mass estimation and class classification of scrap metals using deep learning” (38 citations), introduces a novel AI framework that can both identify and weigh scrap metal pieces in a single step, dramatically improving sorting efficiency. He is also the creator of the “You Only Demanufacture Once (YODO)” methodology (22 citations), which applies unsupervised learning to streamline the retrieval of components from end-of-life electronics. Further demonstrating the practical impact of his work, his techno-economic assessment of robotic aluminium sorting (14 citations) provides critical economic validation for deploying AI-driven systems in the recycling industry, addressing the surging demand for high-quality wrought aluminium alloys. Through these innovations, Díaz‐Romero is establishing the foundational technologies for a more automated, efficient, and economically viable circular economy.