Distinguished Professor of Energy and Power Engineering at Dalian University of Technology and State Key Lab of Structural Analysis, Optimization and CAE Software for Industrial Equipment, and Director of Research Institute for Carbon Neutrality and Provincial Key Lab of Digital Twin for Industrial Equipment at Liaoning. He received his Ph.D. in Structural Engineering from the Department of Civil and Environmental Engineering at The Ohio State University (OSU) in 2005 specializing in intelligent systems, and his MEng in Structural Engineering from the National University of Singapore (NUS) in 2000. From Jan 2005 to Oct 2007, Dr. Jiang worked as a Postdoctoral Researcher at the Department of Civil and Environmental Engineering of Vanderbilt University with a specialty on the predictive analytics and PHM of turbomachines. After then he worked as a Senior Engineer and Technical Leader of AI analytics in Ford Motor, General Electric and Mohawk companies for over 14 years, where he led to drive the AI algorithms development for remote monitoring, analytics, diagnostics, and prognostics of industrial equipment, as well as pilot applications of Predix and Energy IoT in China customers and Qatar LNG plant (Second largest one in the world).

Dr. Jiang’s current research interests include digital twin, AI4Science, and predictive analytics for smart maintenance of turbomachines. He has authored and co-authored more than 100 scientific papers including one book and five book chapters in the cross-disciplinary fields of predictive AI and statistics with engineering and had over 20 Europe, US, and China patents. He has been invited to deliver over 20 keynote speeches and serve as an editorial board or associate editor for more than 50 different international conferences or journals related to smart maintenance and digital twin of turbomachines. Dr. Jiang was an elected member of the ASME VVUQ subcommittee for eight years. He currently is a Chartered Professional Engineer in Structural Engineering in the USA, and a member of specification committee for the Alliance of Chinese International Turbomachinery, Dalian United Research Center of Intelligent Bearing and DLUT AI Research Institute, and the technical committee for Liaoning Innovation Research Institute of Marine Renewable Energy Technology Professional Platform. He has been listed as one of the highly cited Chinese researchers in Mechanical Engineering by Elsevier since 2015, and one of top 2% scientists in the world by Stanford University since 2018.

Day 2: Wednesday, September 9th | 09:45 – 10:30

Room: Opus

Day 1: Wednesday, September 9th | 14:00 – 15:30

Room: Opus

Title:  The AI/ML Ecosystem for Engineering Analysis, Design, and Control

Moderator: Prof. Juan Alonso (Stanford University, USA)

Panelists:
Ass. Prof. Michael Bauerheim (ISAE-Supaero, France), Prof. Xiaomo Jiang  (Dalian University of Technology, China), Ass. Prof. Ricardo Vinuesa (University of Michigan, USA)

Abstract:
As data availability (from simulation, test, and operations) becomes more widely available, AI/ML-based methods for the analysis, design, and control of aerospace systems, including power and propulsion systems, are becoming more pervasive. But the pace of development is frenetic and the landscape of physics-informed and agentic AI/ML is changing more rapidly than we can keep up with. In this panel we tackle both the current state and 1-2-year predictions for the capability, accuracy, and cost for such methods. What are the most logical applications in engineering in the near term? What transformational capabilities do we expect in the next 5 years? What are the key bottlenecks to make this technology work in realistic industrial environments?