Quantifying Uncertainty in Machine Learning-Assisted Measurements
Like any science and engineering field, Instrumentation and Measurement (I&M) is currently experiencing the impact of the recent rise of Artificial Intelligence and in particular Machine Learning (ML). In fact, there is an intertwined relationship between the two: I&M is used to collect data, which are then used to train ML models, which in turn are used in I&M systems. The applications are vast: medical diagnosis, surveillance, fault detection, condition monitoring, digital twins, etc. Uncertainty, which is a fundamental component of measurements including sensing, must be quantified for risk management, better decision making, and assuring the user that the system is trustworthy. In this tutorial, we show how ML is used for indirect measurement, and how to quantify the uncertainty of ML-assisted measurements to design more reliable and practical I&M systems. We cover uncertainty quantification for both ML regression and ML classification. Finally, we go over a few specific examples, both from existing literature and hands on exercises.
Instructor
Shervin Shirmohammadi
Prof. Shervin Shirmohammadi received his Ph.D. in Electrical Engineering in 2000 from the University of Ottawa, Canada, and after spending 3 years in the industry as a senior architect and project manager, joined as Assistant Professor the same University, where since 2012 he has been a Full Professor with the School of Electrical Engineering and Computer Science. He is Director of the Discover Laboratory, doing research in machine learning-assisted measurements, especially vision-based measurement, IoT measurements, and multimedia and network measurements. The results of his research, funded by more than $28 million from public and private sectors, have led to over 400 publications, over 70 researchers trained at the postdoctoral, PhD, and Master’s levels, 30+ patents and technology transfers to the private sector, and four Best Paper awards. He is the founding Editor-in-Chief of the IEEE Open Journal of Instrumentation and Measurement, was the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement from 2017 to 2021, the Associate Editor-in-Chief of IEEE Instrumentation and Measurement Magazine in 2014 and 2015, and is currently on the latter’s editorial board. Dr. Shirmohammadi is an IEEE Fellow “for contributions to multimedia systems and network measurements”, and recipient of the 2019 George S. Glinski Award for Excellence in Research, the 2021 IEEE IMS Distinguished Service Award, and the 2023 IEEE IMS Technical Award “for contributions to the advancement of machine learning-assisted measurements."
Publication Year: 2024