Robert Gao, PhD, MS, BS

Cady Staley Professor of Engineering
Department of Mechanical and Aerospace Engineering
Case School of Engineering
Department of Mechanical and Aerospace Engineering
Case School of Engineering

Select Recognition:

  • ASME Milton C. Shaw Manufacturing Medal, 2023.
  • International Leader Award, Center for International Affairs, Case Western Reserve University, 2022.
  • Distinguished Fellow, International Institute of Acoustics and Vibration (IIAV), 2022.
  • Named one of The 20 Most Influential Professors in Smart Manufacturing, SME, 2020.
  • SME Eli Whitney Productivity Award, 2019.
  • IEEE Best Application in Instrumentation and Measurement Award, 2019.

Distinguished Lecturer:

  • IEEE Instrumentation and Measurement Society, 2014-2017
  • IEEE Electron Devices Society, 2008-2013


  • The International Academy for Production Engineering (CIRP), 2016
  • Society of Manufacturing Engineers (SME), 2014
  • Institute of Electrical and Electronics Engineers (IEEE), 2008
  • American Society of Mechanical Engineers (ASME), 2006
  • Member: Connecticut Academy of Science and Engineering, 2010
  • IEEE Technical Award, IEEE Instrumentation and Measurement Society, 2013
  • Outstanding Associate Editor Award: IEEE Transactions on Instrumentation and Measurement, 2012
  • Multiple Best Paper/Best Student Paper Awards
  • Pratt & Whitney Chair Professorship, University of Connecticut, 2008-2015
  • Research Excellence Award, Department of Mechanical Engineering, University of Connecticut, 2011
  • Outstanding Senior Faculty Award, University of Massachusetts Amherst, 2007
  • Barbara H. and Joseph I. Goldstein Outstanding Junior Engineering Faculty Award, University of Massachusetts Amherst, 1999
  • NSF Early CAREER Award, 1996

Select Professional Service:

  • Guest Editor:
    • Robotics and Computer-Integrated Manufacturing, Special Issue on Digitization and Servitization of Machine Tools in the Era of Industry 4.0, 2022-2023.
    • Journal of Materials Processing Technology, Special issue on Artificial Intelligence in Advanced Manufacturing Processes (AiAMP), 2021-2022.
    • IEEE/ASME Transactions on Mechatronics, Focused Section on AI-Based Monitoring in Smart Manufacturing, 2019-2020.
  • Editorial Board Member:
    • Robotics and Computer Integrated Manufacturing, 2018 – present.
    • International Journal of Computer Integrated Manufacturing, 2018 – present.
    • Nanomanufacturing and Nanometrology, 2017 – present.

Research Information

Research Interests

Professor Gao's research interests are in the areas of signal transduction mechanisms for multi-physics sensing, mechatronic systems design, stochastic modeling, multi-resolution data analysis, and artificial intelligence/machine learning for improving the observability and control of manufacturing processes and product quality. His research integrates analytical, numerical, and experimental methods, and has led to the inventions of miniaturized sensors, high-speed measurement instruments, and AI-based data analytic methods to enhance in-situ monitoring and control of manufacturing processes (e.g., plastic injection molding, sheet metal stamping, microrolling, etc.) and prognosis of product quality and system performance (e.g., aircraft engines, building HVAC, batteries, etc.).

His current research addresses AI-enhanced control, intelligence, and autonomy of hybrid autonomous manufacturing processes (e.g., incremental forming and additive manufacturing), which is part of the recently established NSF Engineering Research Center on Hybrid Autonomous Manufacturing: Moving from Evolution to Revolution (NSF ERC HAMMER). He has published three books, over 400 technical papers (including 200 journal articles), 13 awarded patents, and given more than 120 invited talks.


Books and Book Chapters

  • R. Gao and R. Yan, “Wavelet: Theory and Application for Manufacturing"
    • English Edition, Springer, New York, Dordrecht, Heidelberg, London, ISBN 978-1-4419-1544-3,2011
    • Chinese Edition, Machinery Industry Press, ISBN 978-7-111-61407-4, 2019.
  • L. Wang and R. Gao (Eds.), “Condition Monitoring and Control for Intelligent Manufacturing”, Springer, UK, ISBN 1-84628-268-3, 2006.
  • R. Gao, P. Wang, and R. Yan, “Machine Tool Prognosis for Precision Manufacturing”, in Precision Manufacturing: Metrology (Ed. W. Gao), Springer, 2018.
  • R. Gao and P. Wang, “Sensors to Control Processing and Improve Lifetime and Performance for Sustainable Manufacturing”, in Encyclopedia of Sustainable Technologies, Elsevier, (Ed. M. Abraham), pp. 447-462, DOI: 10.1016/B978-0-12-409548-9.10217-9, May, 2017.

Recent Journal Articles

  • J. Wang, P. Fu, and R. Gao, “Machine vision intelligence for product defect inspection based on deep learning and Hough transform”, Journal of Manufacturing Systems, Vol. 51, pp. 52-60, 2019.
  • X. Zhang, H. Zhang, J. Gao, J. Liu, R. Gao, H. Cao, and X. Chen, “Discrete time-delay optimal control method for experimental active chatter suppression and its closed-loop stability analysis”, ASME Journal of Manufacturing Science and Engineering, Vol. 141, pp. 051003-1-13, 2019.
  • R. Zhao, R. Yan, P. Wang, and R. Gao, “Deep learning and its applications to machine health monitoring”, Mechanical Systems and Signal Processing, vol. 115, pp. 213-237, 2019.
  • P. Cao, Z. Fan, R. Gao, and J. Tang, “Harnessing multi-objective simulated annealing toward configuration optimization within compact space for additive manufacturing”, Robotics and Computer-Integrated Manufacturing, Vol. 57, pp. 29-45, 2019.
  • J. Wang, R. Gao, Z. Yuan, Z. Fan, and L. Zhang, “A joint particle filter and expectation maximization approach to machine condition prognosis”, Journal of Intelligent Manufacturing, Vol. 30, No. 2, pp. 605–621, 2019.
  • C. Sun, P. Wang, R. Yan, R. Gao, and X. Chen, “Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization”, Mechanical Systems and Signal Processing, Vol. 114, pp. 25-34, 2018.
  • J. Zhang, P. Wang, R. Yan, and R. Gao, “Long short-term memory for machine remaining life prediction”, Journal of Manufacturing Systems, Vol. 48, pp. 78-86, 2018.
  • P. Wang, H. Liu, L. Wang, and R. Gao, “Deep learning-based human motion recognition for predictive context-aware human-robot collaboration”, CIRP Annals-Manufacturing Technology, Vol. 67, No. 1, pp. 17-20, 2018.
  • X. Zhang, Y. He, R. Gao, J. Geng, X. Chen, and J. Xiang, “Construction and application of multivariable wavelet finite element for flat shell analysis”, Acta Mechanica Solida Sinica, Vol. 31, No. 4, pp. 391-404, 2018.
  • P. Cao, Z. Fan, R. Gao, and J. Tang, “Design for additive manufacturing: optimization of piping network in compact system with enhanced path-finding approach”, ASME Journal of Manufacturing Science and Engineering, Vol. 140, 0810113-1-15, August, 2018.
  • D. Wu, C. Jennings, J. Terpenny, S. Kumara, and R. Gao, “Cloud-based parallel machine learning for tool wear prediction”, ASME Journal of Manufacturing Science and Engineering, Vol. 140, 041005-1-10, April, 2018.
  • J. Wang, Y. Ma, L. Zhang, R. Gao, and D. Wu, “Deep learning for smart manufacturing: methods and applications”, Journal of Manufacturing Systems, Vol. 48, Part C, pp. 1444-156, 2018.
  • L. Barry, L. Hatchman, Z. Fan, J. Guralnik, R. Gao, and G. Kuchel, “Design and validation of a RFID-based device for routinely assessing gait speed in a geriatrics clinic”, Journal of the American Geriatrics Society, pp. 8614-8618, January, 2018.

For a more complete view of Dr. Gao's credentials and accolades, click here to view his biography on the Case School of Engineering website.



Mechanical Engineering
Technical University of Berlin
Mechanical Engineering
Technical University of Berlin
Central Academy of Arts and Design, Beijing, China