Sohyung Cho

Sohyung Cho is a Lecturer in the Department of Industrial Engineering at the University of Miami's College of Engineering. His research focuses on integrating machine learning techniques with manufacturing processes to improve quality control, tool monitoring, and operational efficiency. Key areas include SVM-based algorithms for geometric tolerance analysis, multisensor fusion systems for real-time tool condition assessment, and control-theoretic approaches to production scheduling.

Cho's work emphasizes predictive modeling in manufacturing contexts, such as tool wear progression under semi-dry/dry conditions and capacity investment decisions under exchange rate uncertainty. He collaborates closely with industry stakeholders to develop solutions that reduce production costs and enhance consistency in manufacturing inspections. Notable projects include web-based open-source systems for cylindricity evaluation and ensemble machine learning frameworks for abnormality detection in milling processes.

His research has been applied to improve the robustness of manufacturing systems through quantitative complexity measures and sensor data integration. Computational experiments highlight his focus on algorithm performance metrics like accuracy and CPU efficiency. While no explicit awards are listed, Cho's contributions have been referenced in patents and widely read on platforms like Mendeley.