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About

Dr. Jorge Chang is a pioneering researcher at the intersection of advanced manufacturing and intelligent systems, with key contributions in autonomous additive manufacturing and telerobotics. His most impactful work, "Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer" (2021, 143 citations), revolutionizes materials development by applying machine learning—specifically Bayesian optimization—to autonomously discover optimal print parameters. This breakthrough dramatically accelerates the traditionally slow, labor-intensive process of 3D printing material exploration, enabling rapid, defect-free fabrication without human trial-and-error. Dr. Chang’s research addresses a critical bottleneck in additive manufacturing, offering a pathway to self-optimizing production systems. Earlier, his foundational work "Telerobotics: problems and research needs" (1988, 9 citations) explored telepresence and helmet-mounted displays, laying groundwork for immersive remote control systems. While less cited, this study demonstrates his long-standing commitment to human-robot interaction and simulation. Dr. Chang’s contributions bridge classical robotics and modern AI-driven manufacturing, earning recognition for advancing both theoretical frameworks and practical automation. His work continues to inspire researchers seeking to integrate data-driven optimization into physical production processes.