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Shuai Huang

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Associate Professor
Industrial & Systems Engineering


Assistant Professor Huang joined the department in 2014. Prior to this appointment, he was an assistant professor at the University of South Florida.


  • Ph.D. in Industrial Engineering, Arizona State University, 2012
  • B.S. in Statistics, University of Science and Technology of China, 2007

Research Statement

Dr. Shuai Huang’s research is driven by challenging data analytics problems, emphasizes innovation in statistics for problem solving, and targets system-level decision-making and quality improvement. He develops methodologies for modeling, monitoring, diagnosis, and prognosis of complex systems, such as the brain connectivity networks, cyber-physics systems, disease progressions (e.g., Alzheimer’s, Type 1 Diabetes, Surgical Site Infection), and manufacturing systems (e.g., MEMS surface topography). He also develops novel statistical and data mining models to integrate the heterogeneous datasets such as neuroimaging, genomics, proteomics, laboratory tests, demographics, and clinical variables, for facilitating scientific discoveries in biomedical research and better decision-making in clinical practices.

Select publications

  1. Yang, H., Huang, Y., Tran, L., Liu, J. and Huang, S. 2016, “On Benefits of Diversity Selection via Bilevel Exclusive Sparsity,” Conference on Computer Vision and Pattern Recognition (CVPR 2016), June 27 – June 30, Las Vegas, Nevada, 2016.
  2. Huang, S. and W. Art Chaovalitwongse, “Computational Optimization and Statistical Methods for Big Data Analytics: Applications in Neuroimaging”, INFORMS Tutorial, Vol. 5, 71-88, 2015.
  3. Lin, Y., Liu, K., Byon, E., Qian, X., Huang, S., 2015, “Domain-Knowledge Driven Cognitive Degradation Modeling for Alzheimer’s Disease,” The SIAM International Conference on Data Mining (SDM 2015), Apr. 30 – May 2, 2015, Vancouver, CA.
  4. Ren, S., Huang, S., Papademetris, X., Onofrey, J. and Qian, X., 2015, “A Scalable Algorithm for Structured Kernel Feature Selection,” The 18th International Conference on Artificial Intelligence and Statistics (AISTAT 2015), May. 9 -12, 2015, San Diego, USA.
  5. Liu, K. and Huang, S., “Integration of Data Fusion Methodology with Degradation Modeling Process to Improve Prognostics”, IEEE Transactions on Automation Science and Engineering, Vol. 13 (1), 344-354, 2014.
  6. Huang, S., Kong, Z.Y. and Huang, W.Z., “High-Dimensional Process Monitoring and Change Point Detection Using Embedding Distributions in Reproducing Kernel Hilbert Space (RKHS),” IIE Transactions, Vol. 46 (10), 999-1016, 2014 (Feature Article of IIE Magazine; Honorable Mention for Best Paper Award of IIE Transactions).
  7. Huang, S., Ye, J., Fleisher, A., Chen, K., Reiman, E., Wu, T., and Li, J., 2013, “A Sparse Structure Learning Algorithm for Bayesian Network Identification from High-dimensional Data,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1328-1342 (impact factor: 4.795).
  8. Huang, S., Li, J., Chen, K., Wu, T., Ye, J., Wu, X., and Li, Y., 2012, “A Transfer Learning Approach for Network Modeling,” IIE Transactions, 44, 915-931 (Best Paper Award of IIE Transactions).
  9. Huang, S., Li, J., Sun, Li., Ye, J., Fleisher, A., Wu, T., Chen, K., and Reiman, E., 2011, “Learning Brain Connectivity of Alzheimer’s Disease by Exploratory Graphical Models,” NeuroImage, 50, 935-949.
  10. Huang, S., Li J., Ye, J., Chen, L., Wu, T., Fleisher, A. and Reiman, E., 2011, “Identifying Alzheimer’s Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis,” Proceedings of Neural Information Processing Systems Conference (NIPS), Dec. 12-17, 2011, Granada, Spain.
  11. Huang, S., Li, J., Ye, J., Fleisher, A., Chen, K. and Wu, T., 2011, “Brain Effective Connectivity Modeling for Alzheimer’s Disease by Sparse Bayesian Network,” The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2011), Aug. 21-24, 2011, San Diego, USA.
  12. Huang, S., Li, J., Sun, L., Ye, J., Chen, K. and Wu, T., 2009, “Learning Brain Connectivity of Azheimer's Disease from Neuroimaging Data,” Proceedings of Neural Information Processing Systems Conference (NIPS), Dec. 7-9, 2009, Vancouver, B.C., Canada.