Biography

Dr. Shuai Huang received a B.S. degree on Statistics from the School of Gifted Young at the University of Science and Technology of China in 2007 and a Ph.D. degree on Industrial Engineering from the Arizona State University in 2012. Shuai’s research focuses on data analytics and AI problems with a particular interest on healthcare applications. His research has been funded by the NSF, NIH, DARPA, Juvenile Diabetes Research Foundation (JDRF), META, and several other research institutes and foundations. Dr. Huang currently serves as Associate Editor for the IISE Transactions in Healthcare Systems Engineering and INFORMS Journal of Data Science.

Education

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

Research Statement

Shuai’s research is driven by challenging data analytics and AI problems, emphasizes innovation in machine learning and AI modeling for complex systems and processes in the connected world, automates the integration of human with these data-driven learning systems, and targets interpretable and explainable decision-makings with discretion of AI ethics and accountability.

He develops methodologies for modeling, monitoring, anomaly detection, diagnosis, and prognosis of complex networked systems, such as brain connectivity networks, manufacturing processes, enterprise systems, cyber-physics systems, and Internet of Things (IoT).

He also develops novel AI and machine learning models to integrate the massive 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.

Working with domain experts, these data-driven learning, data engineering, and decision-making models are applied to a range of applications such as healthcare (precision medicine, disease research, biomarker discovery), neuroscience, system biology, IoT, monitoring and anomaly detection, and transportation (mobility data analysis, user behavior modeling for smart transportation demand management (TDM)).

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.

Honors & awards

• Professional Achievement Award, Division of Data Analytics and Information Systems (DAIS), Institute of Industrial and Systems Engineers (IISE), 2023.
• Teaching Award, Division of Data Analytics and Information Systems (DAIS), Institute of Industrial and Systems Engineers (IISE), 2023.
• Faculty Appreciation for Career Education & Training (FACET) Award, College of Engineering Career Center, University of Washington, 2022.
• Best Paper Award (First Runner-up), IEEE Transactions on Automation Science and Engineering, 2019
• Best Applications Paper Award, IISE Transactions, 2016
• Best Paper Award, IISE Transactions, 2014

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