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About

Sagar Kamarthi is a leading researcher at the intersection of manufacturing engineering and data science. His work addresses the critical skills gap in modern manufacturing, where automation, robotics, and the Industrial Internet of Things (IIoT) demand a workforce proficient in both traditional processes and big data analytics. His highly cited 2021 paper, “Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis” (94 citations), provides a foundational framework for aligning educational curricula with industry needs, making him a key voice in workforce development for smart manufacturing. Earlier in his career, Kamarthi made significant contributions to computer-aided manufacturing (CAM) by developing analytical models for tool path optimization. His foundational 1997 paper on staircase traversal of convex polygonal surfaces, along with the subsequent OPTPATH algorithm (1999), established near-optimal methods for generating efficient NC (numerical control) tool paths. This work, though less cited, represents a core contribution to machining efficiency. Kamarthi’s research uniquely bridges the gap between advanced manufacturing technologies and the human expertise required to leverage them, cementing his impact on both the technical and educational dimensions of Industry 4.0.