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Cheng Chen Ph.D.

Assistant Professor

University of Alabama in Huntsville

cc1115@uah.edu

Biography

Dr. Cheng Chen is an assistant professor in the Department of Industrial & Systems Engineering & Engineering Management (ISEEM). Our group is fascinated by implementing Large Language Models (LLMs) and digital threads to tackle the challenges of complex systems in design and manufacturing. We investigate how engineering changes spread across requirement management domains, emphasizing the importance of managing these changes to prevent failures in complex design systems. Our research aims to understand how different architectures of digital threads are built and controlled to trace design changes across various domains of the product lifecycle. Additionally, our manufacturing research interests span the area of predictive maintenance, synthetic data generation, and generative design. His applied research involves collaborating with experts from academia and industry on projects that use Industrial Internet of Things (IIoT) solutions when designing sustainable manufacturing processes to improve assembly efficiency.

Career Timeline

  • UAH Aug 2024 - Present
    Assistant Professor (Tenure Track)
    University of Alabama in Huntsville; Industrial & Systems Engineering and Engineering Management; Director, Integrated Computing Lab; Co-director, Digital X Lab
  • UGA 2022 - 2024
    Postdoc and Instructor
    University of Georgia; College of Engineering; Supervisor: Dr. Jaime Camelio
  • UGA Jul 2022
    Ph.D., Mechanical Engineering
    University of Georgia; Dissertation: Realization of Inter-Model Connections: Linking Requirements and Computer-Aided Design
  • UGA Aug 2019 - Jul 2022
    Graduate Research Assistant
    University of Georgia; Advisor: Dr. Beshoy Morkos
  • FIT Aug 2018 - Aug 2019
    Research Assistant
    Florida Institute of Technology; Department of Mechanical and Aerospace Engineering
  • FIT Nov 2016
    M.S., Aerospace Engineering
    Florida Institute of Technology; Thesis: A Maximum Entropy Approach to Identifying Important Statistical Moments to Best-Represent Spray Distribution Data
  • BUPT May 2012
    B.E., Mechanical and Automation
    Central College of BUPT; Beijing, China

Acknowledgements

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