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About

Mark Fuge is a researcher whose work spans machine learning for engineering design, soft robotics, and advanced manufacturing — fields where his interdisciplinary contributions have helped bridge computational intelligence and physical fabrication. As guest editor of a landmark 2019 special issue on machine learning for engineering design (42 citations), Fuge has been instrumental in shaping how the engineering community understands and adopts modern ML techniques, including deep neural networks, for design tasks. His work on Bayesian optimization for soft catheter robots (17 citations) demonstrates a sophisticated integration of probabilistic methods with medical device design, advancing minimally invasive surgical tools. Fuge has also made the design process more accessible through tools like the MechProcessor (13 citations), which lowers barriers for novice makers seeking to create printable mechanisms. More recently, his group has pioneered 3D microprinting techniques for soft robotic surgical tools at unprecedented scales — including guidewires for pediatric cardiac interventions and ex situ direct laser writing for multi-actuator soft robots — reflecting a commitment to translating cutting-edge fabrication science into clinically meaningful devices. Across his career, Fuge's research consistently demonstrates how principled computational and manufacturing approaches can together unlock new frontiers in engineering design.