ByungGun Joung successfully defended his PhD dissertation on July 9, 2024 and officially will be depositing his dissertation in July. 

After receiving his Bachelor’s degree in Computer Engineering and Master’s degree in Electrical Engineering at the Korea University, Dr. Joung joined the School of Environmental and Ecological Engineering for his Ph.D. at Purdue under the supervision of Dr. John W. Sutherland. Since he joined the LSM in 2018, Dr. Joung has been leading a group of students working on a project funded by the Wabash Heartland Innovative Network (WHIN) project for next generation manufacturing. He led efforts to advance the development and application of ML/AI for various projects in collaboration with many industry and research partners. During the course of his Ph.D. program, Dr. Joung has authored and co-authored 11 papers that have appeared in prestigious journals and conference proceedings.

Joung’s principal research interests include the development of smart and sustainable manufacturing technologies, particularly prognostics and machine diagnosis for manufacturing equipment. His research concerns not only the industrial applications of machine learning but also the environmental and economic performance of manufacturing systems. The integration of the proposed models and methods into smart manufacturing systems has successfully addressed complex and intricate manufacturing problems, enhancing operational efficiency and reliability. Dr. Joung's dissertation, entitled "Prognostics and Fault Diagnosis for Manufacturing Equipment using Machine Learning”, is largely motivated by proposing AI-driven technologies for manufacturing systems to facilitate advanced maintenance strategies and improve production capabilities. In his dissertation, Dr. Joung aims to achieve the following overarching research goal: developing a robust system using ML/AI technologies to support decision-making for various manufacturing challenges. His work provides new insights and methods to enhance the reliability of manufacturing equipment and improve maintenance decision-making, significantly contributing to the field.