IEM Seminar Series

Industrial Engineering & Management in conjunction with the OSU INFORMS Student Chapter will be sponsoring a Seminar Series in the fall and spring semester. Various topics will be covered by faculty from the department. The series will be held on Wednesdays 3:30pm-5:00pm. Seminar topics, location and a short abstract and bio about the presenters will be available here as it becomes available.

See full-color flyer


Seminar Series
Sponsored by IEM & OSU INFORMS Student Chapter

Date: Wednesday February 18th

Time: 3:30 – 5:00 pm

Location: Engineering North 316

Data Fusion for Complex System Analysis & Improvements

Abstract: The wide applications of automatic sensing devices and computer systems have resulted in a temporally and spatially dense data-rich environment, which provides unprecedented opportunities for quality improvement in various applications including manufacturing, healthcare, and so on. The increasing complexity of data structures raises significant research challenges on data analytics. New methodologies for effective data fusion and information integration to support decision-making are in demand. The first part of this talk will discuss new statistical process monitoring methods for defect-free manufacturing processes. One example of the defect-free manufacturing processes is battery joining in electric vehicles. The performance of an entire battery pack may not be as desired if any battery joint has a low quality connection; hence, perfect interconnections between battery cells are highly needed. In this talk, we will propose a new profile monitoring method, which considers both real-time monitoring accuracy and control opportunity for defect prevention in complex manufacturing processes. The second segment of the talk will cover a study of the statistical methods in handling missing values in healthcare data and developing a decision support system about surgical treatments through effective analysis of patients’ information at early stages. Lastly, this talk will discuss the future of data fusion for system modeling and improvements. Novel application areas and the challenges of these applications will be outlined.

Speaker Bio: Weihong (Grace) Guo is a Ph.D. candidate in the Department of Industrial and Operations Engineering at the University of Michigan. Her research interests are in the areas of statistical quality control and process monitoring, data fusion for manufacturing and healthcare system modeling and improvements, and quality-oriented design and modeling of complex manufacturing systems. During her time as a student in IOE, Guo was awarded the ISERC Quality Control & Reliability Engineering best student paper award finalist, the International Conference on Frontiers of Design and Manufacturing Sciences best paper award, the Rackham Predoctoral Fellowship from the University of Michigan, and the Wilson Prize from the Department of Industrial & Operations Engineering.