Uncertainty-Assisted Trustworthy Decision Making with Artificial Intelligence
Speaker: Professor Shervin Shirmohammadi, University of Ottawa
Date: March 27 2025, Thursday
Time: 11:00 AM – 12:30 PM SGT
Venue: COM1 Seminar Room 1
13 Computing Drive S117417
Abstract:
As Artificial Intelligence (AI) becomes a more prevalent technology in nearly all applications of technology, some directly or indirectly affecting human safety, the issue of making trustworthy decisions based on AI prediction becomes important, and in some cases vital. In this talk, we will describe what uncertainty is, what are its sources in AI, how it can be quantified in both AI regression and AI classification (including LLMs), and how it can be used to catch less-trustworthy AI predictions, leading to more accurate and trustworthy systems that can be deployed in the real world.
Biography:
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 AI-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 80 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 Associate Editor-in-Chief of the IEEE Open Journal of Instrumentation and Measurement, for which he was the Founding Editor-in-Chief from 2021 to 2023, and was the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement from 2017 to 2021.
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”.
