2020 Eric Stern Award

Application of IAI in Quality Assurance

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ASQ Montreal Section


Gina Cody School of Engineering and Computer Science



The champion will receive $500 CAD.


  1. Applicants should be registered as a full time Master of Science (MASc) or Master of Engineering (MEng) student at Quality Systems Engineering at Concordia Institute for Information System Engineering (CIISE) and Industrial Engineering from Mechanical, Aerospace and Industrial Engineering (MAIE), Concordia University, Montreal, QC.
  2. It is an individual project. But, if you have a supervisor, you can get help from your thesis advisor.



Recent increased enthusiasm towards Computer-Integrated Manufacturing (CIM) coupled with developments in smart sensor technologies and advances in communication systems have resulted in simultaneous incorporation of several advanced monitoring and sensing technologies within manufacturing and industrial sectors. Conventional production monitoring, anomaly detection, and quality control techniques are incapable of efficiently coping with the rich information content provided by such high-dimensional data sources. Through this trend in manufacturing technology revolution, the mode of industrial and manufacturing production is gradually moving towards intelligence. In particular, Industrial Artificial Intelligence, referred to as IAI, is positioning itself as the transformative technology of the century addressing key analytical challenges associated with conventional industrial process monitoring solutions in dealing with availability of such high variety, high dimensionality, and high velocity condition-monitoring data.

The 2020 Eric Stern Award focuses on design of novel and innovative inspection and control systems for defect detection. Inspection and control systems in place should be capable of performing timely anomaly detection in final product in real time by designing and developing advanced IAI and Machine Learning (ML) based solutions. The students are responsible to find an appropriate data set. The evaluation will be performed based on the best achieved test accuracy, quality of final report, and presentation.

For more information, contact farnoosh.naderkhani@concordia.ca.