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X-WR-CALDESC:Events for AI in Manufacturing Research Centre
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DTSTART;TZID=America/Toronto:20260715T203000
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SUMMARY:[Webinar] Explainable Prediction of Product Quality in Manufacturing (Speaker: Prof. Eldan Cohen)
DESCRIPTION:Brief Center introduction  \nThe AIM Center aims to realize unmanned manufacturing by integrating advanced artificial intelligence technologies across the entire manufacturing process. Its primary target industries include semiconductors\, displays\, batteries\, biotechnology\, future mobility\, and robotics. \nSeminar Abstract \nIn modern manufacturing\, predicting product quality is crucial for improving yield and reducing material waste\, particularly in high-precision sectors. Specifically\, manufacturing lines are often equipped with numerous sensors that continuously monitor parameters such as temperature\, pressure\, flow rates\, and mechanical properties\, generating large volumes of time-series data capturing the evolving production state. While machine learning models can provide accurate predictions of product quality for such time-series data\, their “black-box” nature limits their utility\, as they often fail to provide the insights needed to diagnose the root causes of defects. This lack of interpretability hinders process improvement and trust in automated systems. \nTo address this gap\, we develop explainable models for predicting product quality from time-series data. Evaluation on real-world data shows our models provide superior performance compared to other explainable baselines\, while providing highly interpretable explanations. \nSpeaker Bio \nEldan Cohen is an Assistant Professor of Industrial Engineering at The University of Toronto and the director of the Optimization and Machine Learning (OptiMaL) Lab. His research interests include machine and deep learning\, heuristic search and optimization\, and natural language processing\, with an emphasis on explainable and human compatible approaches. In addition\, he has worked on applications of these techniques in various domains such as healthcare\, manufacturing\, and autonomous agents. He completed his PhD at the Department of Mechanical and Industrial Engineering at the University of Toronto and was a postdoctoral fellow at the Department of Computer Science and the Vector Institute for Artificial Intelligence.
URL:https://aim.mie.utoronto.ca/event/webinar-explainable-prediction-of-product-quality-in-manufacturing-speaker-prof-eldan-cohen/
LOCATION:https://youtu.be/L0hOfeZjmYQ
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260611T200000
DTEND;TZID=America/Toronto:20260611T210000
DTSTAMP:20260615T060451Z
CREATED:20260609T162101Z
LAST-MODIFIED:20260615T060451Z
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SUMMARY:[Webinar] Advancing Autonomous Manufacturing with Humanoid Robotics: Practical Applications and Future Directions (Speaker: Dr. Yunhan Kim)
DESCRIPTION:Brief Center introduction \nThe AIM Center aims to realize unmanned manufacturing by integrating advanced artificial intelligence technologies across the entire manufacturing process. Its primary target industries include semiconductors\, displays\, batteries\, biotechnology\, future mobility\, and robotics. \nOverview of this seminar \nThis seminar introduces the application of humanoid robotics as a key technology for flexible and autonomous manufacturing. It covers practical use cases of manufacturing automation\, ranging from foundational tasks like object picking to complex\, process-oriented scenarios. The session also highlights the core enabling technologies behind these developments\, including perception\, motion control\, and robot learning. It concludes with a discussion on future directions toward general-purpose robotic intelligence\, focusing on how foundation-model-based approaches can enhance adaptability and scalability in production environments. \nSeminar Abstract \nHumanoid robots are emerging as a key technology for flexible and autonomous manufacturing. Unlike conventional automation systems\, humanoid robots can operate in human-centered environments and perform diverse manipulation tasks with minimal changes to existing production facilities. \nThis talk introduces practical applications of humanoid robotics for manufacturing automation\, from basic task demonstrations such as object picking and block stacking to process-oriented use cases in real manufacturing scenarios. Key enabling technologies behind these applications will also be highlighted\, including perception\, motion control\, and robot learning. \nThe talk will conclude with a discussion on future directions toward more general-purpose robotic intelligence\, including foundation-model-based approaches for improving adaptability and scalability in manufacturing environments. \nSpeaker Bio \nYunhan Kim is a Senior Researcher in the Autonomous Manufacturing Research Center at the Korea Electronics Technology Institute (KETI). He earned his B.S. and Ph.D. degrees in Mechanical and Aerospace Engineering from Seoul National University\, South Korea\, in 2016 and 2022\, respectively. He previously served as a Staff Engineer at Samsung Electronics and a Visiting Scholar at the Department of Mechanical and Industrial Engineering\, University of Toronto\, Canada. His research interests are centered on applying industrial AI to advance autonomous manufacturing systems\, with a focus on foundation models for manufacturing and robotics. He currently leads the technical development of several national R&D programs\, spanning manufacturing-specialized AI foundation models\, humanoid robots for autonomous manufacturing\, and on-device generative AI for quality inspection. His research has been recognized with awards from the PHM Society\, the Korean Society of Mechanical Engineers (KSME)\, the Society for Computational Design and Engineering (CDE)\, and the Korean Society of Manufacturing Technology Engineers (KSMTE). \nRelevant Link / SNS/ Youtube: https://www.linkedin.com/in/yunhan-kim/
URL:https://aim.mie.utoronto.ca/event/webinar-advancing-autonomous-manufacturing-with-humanoid-robotics-practical-applications-and-future-directions-speaker-dr-yunhan-kim/
LOCATION:https://youtu.be/Y0vXJUud1aE
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260521T203000
DTEND;TZID=America/Toronto:20260521T213000
DTSTAMP:20260615T060153Z
CREATED:20260507T033054Z
LAST-MODIFIED:20260615T060153Z
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SUMMARY:[Webinar] Teaching AI to Understand Images with Language: Foundations and Advances in Vision–Language Models (Speaker: Prof. Il-Min_Kim)
DESCRIPTION:Brief Center introduction  \nThe AIM Center aims to realize unmanned manufacturing by integrating advanced artificial intelligence technologies across the entire manufacturing process. Its primary target industries include semiconductors\, displays\, batteries\, biotechnology\, future mobility\, and robotics. \nOverview of this seminar \nThis seminar introduces vision–language models\, a type of artificial intelligence that connects images with natural language. It explains the basic ideas behind how these models learn from image–text data. The session also highlights practical applications such as image recognition and search\, while discussing important challenges like bias\, difficulty with unfamiliar objects\, and high computational costs. It concludes with an overview of recent research aimed at improving fairness\, robustness\, and efficiency\, as well as enhancing the models’ ability to capture detailed visual information. \nSeminar Abstract \nRecent advances in artificial intelligence (AI) have enabled models to learn from both images and natural language. Vision–Language Models allow computers to connect visual concepts with textual descriptions\, making it possible to recognize objects\, retrieve images\, and perform complex visual reasoning tasks using natural language prompts. One of the most influential models in this area is Contrastive Language-Image Pre-Training (CLIP)\, which learns visual concepts from large collections of image–text pairs. \n  \nIn this talk\, we introduce the basic ideas behind vision–language models and explain how CLIP works. We then discuss several challenges that arise when deploying these systems in real-world environments\, such as biased predictions\, difficulty recognizing unfamiliar objects\, and the computational cost of adapting large models. Finally\, we present recent research that addresses these challenges by improving fairness\, robustness\, and efficiency in vision–language models\, as well as new methods that enhance their ability to capture fine-grained visual details. \nSpeaker Bio \nIl-Min Kim received his B.S. degree in Electronics Engineering from Yonsei University\, Seoul\, Korea\, in 1996\, and his M.S. and Ph.D. degrees in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST)\, Taejon\, Korea\, in 1998 and 2001\, respectively. He then worked as a Postdoctoral Research Fellow in the Department of Electrical Engineering and Computer Sciences (EECS) at the Massachusetts Institute of Technology (MIT) from October 2001 to August 2002 and in the Department of Electrical Engineering at Harvard University from September 2002 to June 2003. In July 2003\, he joined the Department of Electrical and Computer Engineering (ECE) at Queen’s University\, Kingston\, Canada\, and he is currently serving as Head of the ECE Department. \n  \nHis research focuses on artificial intelligence (AI)\, including agentic AI\, physical AI\, ubiquitous AI\, edge AI\, on-device AI\, safe AI\, universal equity AI\, AI governance\, AI alignment with human values\, foundation models\, AI for healthcare applications\, machine unlearning\, data privacy in machine learning\, federated learning\, distributed learning\, continual learning\, diffusion models\, out-of-distribution (OOD) detection\, self-supervised learning\, contrastive representation learning\, AI for IoT/IoE/IIoT/Mobile Crowd Sensing (MCS)\, AI-driven 6G wireless systems\, AI-driven vehicle-to-everything (V2X) communications\, and Geoscience AI (Geo-AI). \n\n 
URL:https://aim.mie.utoronto.ca/event/webinar-teaching-ai-to-understand-images-with-language-foundations-and-advances-in-vision-language-models-speaker-prof-il-min_kim/
LOCATION:https://youtu.be/OxiyrPW_Ye4
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260407T203000
DTEND;TZID=America/Toronto:20260407T213000
DTSTAMP:20260414T155843Z
CREATED:20260331T175338Z
LAST-MODIFIED:20260414T155843Z
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SUMMARY:[Webinar] Robotic Manipulation and Physics-Informed Machine Learning (Speaker: Prof. Yu Sun)
DESCRIPTION:Brief Center introduction  \nThe AIM Center aims to realize unmanned manufacturing by integrating advanced artificial intelligence technologies across the entire manufacturing process. Its primary target industries include semiconductors\, displays\, batteries\, biotechnology\, future mobility\, and robotics. \nOverview of this seminar \nRobots play a central role in modern manufacturing\, yet manipulating and assembling micro-scaled objects remains largely manual. This seminar\, presented by Professor Yu Sun from the University of Toronto\, will introduce the field of robotic micromanipulation and explore how physics-informed machine learning can enhance robotic control at small scales. Using real-world examples\, the talk will highlight how combining data-driven approaches with physics-based models can improve precision and efficiency in micro-assembly processes. \nSeminar Abstract \nRobots have become an integrated component in manufacturing. Although robotic manipulation at the macro scale has transformed many manufacturing processes\, the manipulation and assembly of micro-scaled objects is still largely performed manually. This talk will start with an introduction of robotic micromanipulation and then migrate from dynamics-driven closed-loop control to physics-informed machine learning.  Specific cases will be used to illustrate the symbiosis of data-driven and physics model-driven methods. \nSpeaker Bio \nYu Sun is a Professor at the University of Toronto. He is a Fellow of all three of the national academies in Canada — the Canadian Academy of Engineering\, the Royal Society of Canada and the Canadian Academy of Health Sciences. He is also an International Member of the Chinese Academy of Engineering and the U.S. National Academy of Engineering. His research focuses on robotics and precision instrumentation. \nRelevant Link / SNS/ Youtube : https://sun.mie.utoronto.ca/
URL:https://aim.mie.utoronto.ca/event/webinar-robotic-manipulation-and-physics-informed-machine-learning-speaker-prof-yu-sun/
LOCATION:https://www.youtube.com/watch?v=CvPZB9ZXuqk&list=PLYfUBvYcaM8OeqJm8Yko3gahwOGNS2Kgh
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