Dr. Yongzhi Qu | Scientific Machine Learning | Excellence in Innovation

University of Utah | United States

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Biography of Dr. Yongzhi Qu

🏅 Assistant Professor at University of Utah | Ph.D. in Industrial Engineering & Operations Research

Dr. Yongzhi Qu is an accomplished assistant professor at the University of Utah in the Department of Mechanical Engineering, specializing in AI-powered systems, data-driven dynamics, autonomous manufacturing, and digital twins. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a focus on Industrial Engineering & Operations Research. His research spans several fields, including machine learning, system modeling, and control for mechanical and structural systems.

📚 Education

• Ph.D. in Industrial Engineering & Operations Research – University of Illinois at Chicago (2014)
• M.S. in Measurement & Testing Technology – Wuhan University of Technology (2011)
• B.Sc. in Measurement & Control Instrumentation and Technology – Wuhan University of Technology (2008)

💼 Professional Experience

• Assistant Professor (07/2023 – Present)
  Department of Mechanical Engineering, University of Utah
• Assistant Professor (08/2019 – 06/2023)
  Department of Mechanical & Industrial Engineering, University of Minnesota Duluth
• Assistant/Associate Professor (01/2015 – 07/2019)
  Department of Mechanical Engineering, Wuhan University of Technology
• Application Engineer (12/2013 – 12/2014)
  The DEI Group, Millersville, Maryland, US

🧠 Research Interests ON Scientific Machine Learning

Dr. Qu’s research focuses on scientific machine learning, AI-powered system modeling, estimation, and control for dynamic systems, with applications in autonomous manufacturing and digital twins. His recent work explores the intersection of machine learning, physics, and mathematics to model and control complex systems.

🏆 Research Grants

• A Neural Differential Machine Learning Framework with Nonlinear Physics
  National Institute of Standards and Technology (NIST), $121,015 (2023-2026)
• Real-time System Identification for Machining Spindles
  NIST, $159,950 (2020-2022)
• Learning Real-time Dynamics of a Rotor System
  University of Minnesota, $44,501 (2020-2021)

🏅 Academic Awards

• Best Academic Paper Award (IEEE International Conference on Prognostics and Health Management, 2013)
• Best Student Paper Award (Society for Machinery Failure Prevention Technology Conference, 2014)
• Best Paper Award (Prognostics and System Health Monitoring Conference, 2018)

📢 Invited Talks

• Machine Learning for Dynamic System Modeling, Seagate (2022)
• Keynote on Deep Learning in PHM, Annual Conference of PHM Society (2019)
• FBG Sensing for Machinery Health Monitoring, Northeastern University, China (2016)

🎓 Teaching

• Machine Learning for System Dynamics and Control, University of Minnesota Duluth
• Six Sigma and Quality Control, University of Minnesota Duluth
• Control Engineering, Wuhan University of Technology

🤝 Professional Service

• Organizing Chair, Data Challenge, 15th Annual Conference of PHM Society (2023)
• Panelist, Doctoral Symposium, 14th Annual Conference of PHM Society (2022)
• Symposium Chair, ASME Manufacturing Science and Engineering Conference (2022, 2023)

📚 TOP NOTES PUBLICATIONS 

State space neural network with nonlinear physics for mechanical system modeling

• • Authors: Reese Eischens, Tao Li, Gregory W. Vogl, Yi Cai, Yongzhi Qu
  • Journal: Reliability Engineering & System Safety
  • Year: 2025
  • DOI: 10.1016/j.ress.2025.110946

Graph neural network architecture search for rotating machinery fault diagnosis based on reinforcement learning

• • Authors: Jialin Li, Xuan Cao, Renxiang Chen, Xia Zhang, Xianzhen Huang, Yongzhi Qu
  • Journal: Mechanical Systems and Signal Processing
  • Year: 2023
  • DOI: 10.1016/j.ymssp.2023.110701

Development of Deep Residual Neural Networks for Gear Pitting Fault Diagnosis Using Bayesian Optimization

• • Authors: Jialin Li, Renxiang Chen, Xianzhen Huang, Yongzhi Qu
  • Journal: IEEE Transactions on Instrumentation and Measurement
  • Year: 2022
  • DOI: 10.1109/TIM.2022.3219476

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

• • Authors: Jialin Li, Xueyi Li, David He, Yongzhi Qu
  • Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
  • Year: 2020
  • DOI: 10.1177/1748006X19867776

Gear pitting fault diagnosis using disentangled features from unsupervised deep learning

• • Authors: Yongzhi Qu, Yue Zhang, Miao He, David He, Chen Jiao, Zude Zhou
  • Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
  • Year: 2019
  • DOI: 10.1177/1748006X18822447