Join the Mechanical and Aerospace Engineering Department for a distinguished lecture featuring Dr. Jay Lee, a Clark Distinguished Professor and Director of the Industrial AI Center at the University of Maryland, College Park! He will be presenting, "Recent Advances of Industrial AI for Highly Connected and Complex Engineering Systems and Perspectives on Data-Centric Engineering Education." Coffee and tea will be served at 1pm, with the lecture beginning at 2pm!

Abstract
Artificial intelligence (AI) is one of the most powerful technologies of our time. AI embodies a field within cognitive science that enables the discovery of numerous intelligent methods for emulating human sensory and cognitive functions. Industrial artificial intelligence (Industrial AI) is a system engineering approach to bring about high value impacts in speed and scale in broad applications with capabilities to predict and avoid the invisible problems, ultimately creating a worry-free and resilient industrial system. This presentation will introduce the trends and recent advances of Industrial AI for improved resilience of complex and highly connected industrial systems. First, trends of data-centric industrial systems and unmet needs of productivity are introduced. Next, some recent advances of
industrial AI and non-traditional machine learning including topological data analytics, stream-of-quality (SoQ) based data analytics, similarity-based machine learning, domain adaptation and transfer learning, etc. for highly connected and complex industrial systems will be introduced with some examples including semiconductor manufacturing, etc. Furthermore, perspectives on Industrial Large Knowledge Model and data-centric engineering education through data foundry for future workforce and talents will be discussed.

Bio
Dr. Jay Lee is Clark Distinguished Professor and Founding Director of Industrial AI Center in the Mechanical Engineering of the Univ. of Maryland College Park. His current research is focused on developing non- traditional machine learning technologies including transfer learning, domain adaptation, similarity-based machine learning, stream-of-x machine learning, as well as industrial large knowledge model (ILKM), etc. In addition, he is leading AI Foundry and Data Foundry which consist of over 30 different machine learning analytic tools and 100 diversified industrial datasets including semiconductor manufacturing, jet engines, wind turbine, EVs, high speed train, machine tools, robots, medical TBI, etc. for rapid development and deployment of AI.