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Photo of Huang, Jida

Jida Huang

Assistant Professor

Department of Mechanical and Industrial Engineering


Building & Room:

3029 ERF


842 W. Taylor Street, Chicago, Illinois 60607

Office Phone:



Related Sites:


Research Interests:

Data-Driven Design: Extract geometric pattern from high volume and variety of design data,
together with geometric deep learning methods to generate customized design and implement
design automation and generative design.

Cyber Physical System: Through sensor data fusion and data analytics, monitor and control
the process of additive manufacturing to achieve 3D printed products with consistent quality,
and explore a cyber-manufacturing system.

Advanced Manufacturing System: Analyze the manufacturing data in design, fabrication
stages and integrate deep learning techniques to improve the accuracy and efficiency for
situation-dependent decision making in manufacturing system.

Selected Publications

  1. Huang, J., Kwok, T.H. and Zhou, C., (2019). Parametric Design for Human Body Modeling by Wireframe-Assisted Deep Learning, Computer-Aided Design, 108, pp.19-29.
  2. Huang, J., Kwok, T.H. and Zhou, C., (2019). Surfel convolutional neural network for support detection in additive manufacturing, International Journal of Advanced Manufacturing Technology, Accepted.
  3. Zhang, B., Huang, J., Rai, R. and Manjunatha, H., (2018). A Sequential Sampling Algorithm for Multistage Static Coverage Problems. ASME Transactions, Journal of Computing and Information Science in Engineering, 18(2), pp.021016.
  4. Huang, J., Kwok, T.H. and Zhou, C., (2017). V4PCS: Volumetric 4PCS algorithm for global registration. ASME Transactions, Journal of Mechanical Design, 139(11), pp.111403.
  5. He, X., Huang, J., Rao, Y. and Gao, L., (2016). Chaotic teaching-learning-based optimization with Lévy flight for global numerical optimization. Computational intelligence and neuroscience, vol. 2016, Article ID 8341275.
  6. He, X., Rao, Y. and Huang, J., (2016). A novel algorithm for economic load dispatch of power systems. Neurocomputing, 171, pp.1454-1461.
  7. Yi, W., Li, X., Gao, L., Zhou, Y. and Huang, J., (2016). ε constrained differential evolution with pre-estimated comparison using gradient-based approximation for constrained optimization problems. Expert Systems with Applications, 44, pp.37-49.
  8. Huang, J., Gao, L. and Li, X., (2015). An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Applied Soft Computing, 36, pp.349-356.
  9. Huang, J., Li, X. and Gao, L., (2013). A new hybrid algorithm for unconstrained optimization problems. International Journal of Computer Applications in Technology, 46(3), pp.187-194.
  10. Gao, L., Huang, J. and Li, X., (2012). An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process. Applied Soft Computing, 12(11), pp. 3490-3499.


Ph.D., Industrial and Systems Engineering
University at Buffalo, 2019

M.S., Industrial Engineering,
Huazhong University of Science and Technology, 2014

B.Eng., Industrial Engineering,
China University of Mining and Technology, 2011