Zheng hosted AI/ML for Complex Engineering Systems session at 2025 INFORMS

Oct 29, 2025 · 1 min read

Zheng is incredibly grateful to our outstanding invited speakers for making our 2025 INFORMS session “AI/ML for Complex Engineering Systems” (invited sub-session under Quality, Statistics & Reliability track) such a resounding success! Thank you Professor Jian Hu, Professor Gökçe Dayanıklı, Gulai Shen, and Jaeshin Park for sharing your insights. Your contributions are driving advancements in the application of artificial intelligence and machine learning to the most challenging engineering problems. It was a privilege to host this session with Professor Shancong Mou (who is also Zheng’s undergraduate friend). Looking forward to more opportunities to learn and innovate together!

Zheng Liu
Authors
Zheng Liu
Assistant Professor
Zheng Liu joined the University of Michigan-Dearborn Industrial and Manufacturing Systems Engineering Department as an Assistant Professor. His research bridges theoretical and applied aspects of AI in manufacturing and energy systems. He focused on physics-based modeling, physics-informed machine learning, and generative design for manufacturing and energy storage applications. He also evaluated the manufacturing processes through life cycle assessment and techno-economic analysis, helping the team win $1M Department of Energy’s (DOE) inaugural American-Made Geothermal Lithium Extraction Prize. He has collaborated on multiple projects funded by the National Science Foundation (NSF) through the Future Manufacturing Research Grant (FMRG) program, as well as the Office of Naval Research (ONR). In addition, he served as a graduate fellow and STEM outreach coordinator at the NSF Engineering Research Center for Power Optimization for Electro-Thermal Systems (POETS). Zheng has received the Sharp Outstanding Graduate Student Award and the Tau Beta Pi Outstanding Graduate Student Award in recognition of his research contributions. He has published more than 15 peer-reviewed journal articles and 15 conference proceedings, and has served as a track chair for the IEEE Transportation Electrification Conference. Through his work, he continues to push the frontiers of AI-driven innovation in manufacturing and energy systems.