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About

Sean Hanna is a researcher whose work sits at the intersection of robotics, machine learning, and computational design, with a particular focus on advancing autonomous and adaptive systems for construction and manufacturing. His most significant contributions lie in the development of adaptive robotic methods for subtractive manufacturing, notably his pioneering work on training robotic systems to process timber using sensor feedback and machine-learning techniques — research that has garnered nearly 40 citations across related publications. Hanna has also made notable strides in autonomous construction, exploring how Building Information Modelling (BIM) can enhance object recognition for on-site robotic assembly in unstructured environments, work that has attracted 18 citations and addresses a critical bottleneck in construction automation. His earlier investigations into stereolithography and shape memory alloys reflect a longstanding interest in blurring the boundaries between structure and actuation in robotic design. Complementing his applied research, Hanna has also engaged with foundational questions in spatial reasoning and three-dimensional representation relevant to design practice. Collectively, his body of work positions him as a thoughtful contributor to the growing field of intelligent, sensor-driven fabrication and autonomous construction robotics.