Dr. Zhenghui Sha is an Assistant Professor in the Walker Department of Mechanical Engineering at the University of Texas (UT) at Austin. Before joining UT in 2021, he was an Assistant Professor in the Department of Mechanical Engineering at the University of Arkansas from 2017 to 2020 and a Postdoctoral Fellow in the McCormick School of Engineering at Northwestern University in 2016. Dr. Sha received a Ph.D. in Mechanical Engineering from Purdue University. He also holds a Graduate Certificate in Applied Statistics and an undergraduate minor in Computer Science. Dr. Sha’s research focuses on system science and design science as well as the intersection between these two areas. He aims to build the decision-centric science foundation for complex systems engineering and design. Such a foundation will facilitate the integration of complex systems and amplify the data-driven power in engineering design and design research. In particular, his research projects span across design theory, artificial intelligence (AI) for design, human-machine interaction, swarm manufacturing, and complex sociotechnical systems, such as transportation systems and electric vehicle (EV) charging networks. He is a core faculty member in the Advanced Manufacturing and Design Area, a faculty member of the Center for Additive Manufacturing and Design Innovation (CAMDI), and an affiliated faculty member of the Oden Institute of Computational Engineering and Sciences.
Dr. Sha was named Walker Scholar by the Walker Department of Mechanical Engineering at UT in 2024 and was the recipient of the 2022 Young Engineering Award (YEA) from the Computers & Information in Engineering (CIE) Division of the American Society of Mechanical Engineers (ASME). He received the Best Dissertation of The Year Award in 2017 from the ASME CIE Division and was the recipient of the 2023 ASME Journal of Mechanical Design (JMD) Editor’s Choice (Best Paper) Honorable Mention. He received the ASME Robert E. Fulton Best Paper Award twice from the Systems Engineering Information & Knowledge Management (SEIKM) Technical Committee in 2013 and 2017, respectively. He received the Best Faculty Advisor Award with Honorable Mention from UT ME, the 2021 Outstanding Faculty Members of the Year Award, the 2020 Outstanding Teaching Award, and the Open Education Resources Initiative Award from the University of Arkansas. He was the Reviewer with the Distinction Award for JMD and received the Technical Committee Leadership Award from the ASME CIE Division in 2020. Dr. Sha is the author of more than 100 peer-reviewed journal and conference publications and four book chapters. He is the inaugural Chair of the ASME CIE Hackathon and has been a member of the ASME CIE Hackathon Committee since 2019. He served as the Chair of the ASME SEIKM Technical Committee from 2018 to 2019 and the Chair of the ASME AI and Machine Learning Technical Committee from 2024 to 2025. He has been serving on the Executive Committee of the ASME Design Automation Conference (DAC) since 2024. His collaborative research on swarm manufacturing was featured by 3DPrintingIndustry.com, General Electric, and Henry Ford’s Innovation Nation with Mo Rocca of CBS. He teaches courses on machine elements, design methodology, and data-driven design and decision-making in complex systems.
Selected Publications
- X. Li, Y. Sun, Z. Sha, “LLM4CAD: Multi-Modal Large Language Models for Three-Dimensional Computer-Aided Design Generation,” Journal of Computing and Information Science in Engineering, volume 25, issue 2, pp: 021005 (14 pages), Feb 2025. https://doi.org/10.1115/1.4067085
- H. O. Demirel, M. Goldstein, X. Li, and Z. Sha, “Human-Centered Generative Design Framework: An Early Design Framework to Support Concept Creation and Evaluation,” International Journal of Human-Computer Interaction, pp: 1-12, 2023. https://doi.org/10.1080/10447318.2023.2171489
- M. Rahman, C. Xie, Z. Sha, “Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks,” Journal of Mechanical Design, volume 143, issue 8, pp: 081706 (12), August 2021. https://doi.org/10.1115/1.4049971
- A. E. Bayrak, Z. Sha, “Integrating Sequence Learning and Game Theory to Predict Design Decisions under Competition,” Journal of Mechanical Design, volume 143, issue 5, pp: 051401 (9), May 2021. https://doi.org/10.1115/1.4048222
- L. Poudel, W. Zhou, Z. Sha, “Resource-Constrained Scheduling for Multi-Robot Cooperative 3D Printing,” Journal of Mechanical Design, volume 143, issue 7, pp: 072002 (12), July 2021. https://doi.org/10.1115/1.4050380
- M. Rahman, C. Schimpf, C. Xie, Z. Sha, “A Computer-Aided Design Based Research Platform for Design Thinking Studies,” Journal of Mechanical Design, volume 141, issue 12, pp: 121102 (12), 2019. https://doi.org/10.1115/1.4044395
- Z. Sha, Y. Huang, S. J. Fu, M. Wang, N. Contractor, Y. Fu, W. Chen, “A Network-Based Approach to Modeling and Predicting Product Co-Consideration Relations,” Complexity, volume 2018, Article ID 2753638, 14 pages, 2018. https://doi.org/10.1155/2018/2753638
- Z. Sha, K. A. Moolchandani, J. H. Panchal and D. A. DeLaurentis, “Modeling Airlines’ Decisions on City-Pair Route Selection Using Discrete Choice Models,” AIAA Journal of Air Transportation, volume 24, No. 3, pp. 63-73, 2016. https://doi.org/10.2514/1.D0015
- Z. Sha, K. N. Kannan, J. H. Panchal, “Behavioral Experimentation and Game Theory in Engineering Systems Design,” Journal of Mechanical Design, volume 137, issue 5, pp: 051405 (10), 2015. https://doi.org/10.1115/1.4029767
- Z. Sha, J. H. Panchal, “Estimating Local Decision-Making Behavior in Complex Evolutionary Systems,” Journal of Mechanical Design, volume 136, issue 6, pp: 061003 (11), 2014. https://doi.org/10.1115/1.4026823
