Webinar | AI-Enabled Sensing and Controls for Digital Twins and Industrial Automation
The potential to enable scalable AI-enabled control systems and digital twin frameworks for intelligent manufacturing, environmental monitoring, energy systems, and intelligent mobility underscores the need for designs that are robust to uncertainty, especially in time- and safety-critical scenarios. However, multimodal sensing, process understanding, and AI-driven decision making remain challenging due to computational limits, environmental uncertainty, and strict safety and quality constraints.
This talk will focus on research at the University of Alberta's Networked Optimization, Diagnosis, and Estimation (NODE) lab, aimed at improving reliability and efficiency of AI-based monitoring, fault detection, predictive maintenance, and process control using augmented multimodal sensing. It will also provide intuition on resilient control and AI-based state estimation using distributed sensing for real-time process monitoring, and autonomous process control, with applications in intelligent mobility, natural resources sector, and robotic fabrication. Performance will be discussed in terms of efficiency and accuracy for reliable monitoring and large-scale sensor deployments.
Join the Fort McMurray Branch for this event.
For any questions about this event, please contact [email protected].
About the presenter
Dr. Ehsan Hashemi is an associate professor in the Department of Mechanical Engineering at the University of Alberta (since 2021). He received his PhD in Mechanical and Mechatronics Engineering from the University of Waterloo (Canada). He previously served as a research assistant professor at the University of Waterloo and as a visiting professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology (Sweden).
His research focuses on AI-based control and digital twins for intelligent manufacturing, integrating learning-aided monitoring, and predictive maintenance to enable resilient and adaptive industrial systems. He develops data-driven and physics-informed models, as well as multimodal sensing systems. His work also advances digitization frameworks for autonomous fabrication systems, combining networked sensing, and learning-enabled control for scalable and reliable operation. In addition, he investigates environmental monitoring using multimodal distributed sensing and sensor deployment, enabling coordinated perception in harsh weather and complex environments. Dr. Hashemi collaborates extensively with Canadian and international industry partners on intelligent mobility, augmented multimodal sensing systems, and intelligent process optimization, with multiple technology transfers. He is a Senior Member of IEEE, with expertise spanning control theory, cyber-physical systems, and robot learning.
This event applies to the following Work Readiness Program skill area:
