Time:March 18, 2026 (Wednesday) 16:00-18:00

Venue:Room 5008, Building 2, iHarbor

Professor Shervin Shirmohammadi,an IEEE Fellow, has received numerous international honors including the 2023 IEEE Instrumentation and Measurement Society (IMS) Technical Award, the 2021 IEEE IMS Distinguished Service Award, and the 2019 George S. Glinski Award for Excellence in Research. He is currently a Professor at the School of Electrical Engineering and Computer Science, University of Ottawa, Canada, and serves as the Director of the Discover Laboratory. His main research interests lie in the intersection of artificial intelligence and measurement science, including AI-assisted measurement, classification and large language model uncertainty, human activity recognition, network diagnostics and operations, and IoT measurement. His research has secured over $28 million CAD in funding from public and private sectors, and holds more than 30 patents and technology transfers. He has authored over 450 academic papers (winning 4 Best Paper Awards), served as the Editor-in-Chief of the prestigious IEEE Transactions on Instrumentation and Measurement (2017-2021), and was the Founding Editor-in-Chief of the IEEE Open Journal of Instrumentation and Measurement (2022-2023).

Lecture 1：Uncertainty-Assisted Trustworthy Decision Making with Artificial Intelligence

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. Measurement is a fundamental and key enabler of AI, because measurement is used to collect data, which is then used to train an AI model, which in turn is used for indirect measurement such as detection, tracking, monitoring, characterization, identification, sensing, estimation, recognition, or diagnosis of a physical phenomenon. In this talk, we will learn about the concept of uncertainty and how it can make AI systems more trustworthy for real-world deployment. We will study uncertainty from the perspective of both measurement standards, such as VIM and GUM, and AI paradigms of regression, classification, and Large Language Models (LLM). Finally, we go over a few specific examples from existing literature.

Lecture 2：Tips for scientific paper writing and publication in IEEE

In this short talk, we go over some considerations about presenting the result of your research as a paper to be published in an IEEE periodical. Topics include selection of the most effective and relevant periodical, writing of the paper, what to expect during the review process, how to respond to reviewers, and AI-generated content.

（Stamps available）