Shuai Huang

Machine Learning, Healthcare, and Systems Engineering

Methodology (Machine Learning/Analytics/Decision Making)

  • [IIE] Lin1, Y., Liu, S. and Huang, S., “Selective Sensing of A Heterogeneous Population of Units with Dynamic Health Conditions”, IIE Transactions, 2018.

  • [EURASIP] Samareh1, A., Huang, S., “DL-CHI: a Dictionary Learning-based Contemporaneous Health Index for Degenerative Disease Monitoring”, EURASIP Journal of Advances in Signal Processing, 2018.

  • [IEEE-PAMI] Ren, S., Huang, S., Ye, J. and X, Qian., “Safe Feature Screening for Generalized LASSO”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.

  • [IEEE-Reliability] Lin1, Y., Liu, K., Byon, E., X, Qian., Liu, S. and Huang, S., “A Collaborative Learning Framework for Estimating Many Individualized Regression Models in a Heterogeneous Population”, IEEE Transactions on Reliability, 2017.

  • [IEEE-ASE] Xiao1, C., Jin1, Y., Liu, J., Zeng, B. and Huang, S., “Optimal Expert Knowledge Elicitation for Bayesian Network Structure Identification”, IEEE Transactions on Automation Science and Engineering, 2018.

  • [IIE] Jin1, Y., Huang, S., Wang, G. and Deng, H., “Diagnostic Monitoring of Multivariate Process via a LASSO-BN Formulation”, IIE Transactions, 2017.

  • [IEEE-TPS] You, M., Liu, B., Byon, E., Huang, S., Jin, J., “Direction-dependent Power Curve Modeling for Multiple Interacting Wind Turbines”, IEEE Transactions on Power Systems, 2017.

  • [IEEE-ASE] Sun, HY., Huang, S. and Ran, J, “Functional Graphical Models for Manufacturing Process Modeling”, IEEE Transactions on Automation Science and Engineering, 2017.

  • [IEEE-TIP] Lu, W., Cheng, Y., Xiao1, C., Chang, S., Huang, S., Liang, B. and Huang, T. “Unsupervised Sequential Outlier Detection with Deep Architectures”, IEEE Transactions on Image Processing, 2017.

  • [SDM] Nie, Z., Lin, B., Huang, S., Ramakrishnan, N., Fan, W. and Ye, J. “Pruning Decision Trees via Max-Heap Projection”, The SIAM International Conference on Data Mining (SDM 2017), Apr. 27 – Apr. 29, 2017, Houston, TX. (paper acceptance rate < 25 %).

  • [IJCAI] Wang, Z., Huang, S., Zhou, J. and Huang, T. “Doubly Sparsifying Network”, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), August 19-25, 2017, Melbourne, Australia. (paper acceptance rate = 25.9% %)

  • [SMJ] Li, M., Lin1, Y., Huang, S. and Crossland, C., “The Use of Sparse Inverse Covariance Estimation for Relationship Detection and Theory Building in Strategic Management”, Strategic Management Journal, Vol. 37 (1), 86-97, 2016.

  • [CVPR] 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. (paper acceptance rate 29.9 %)

  • [IEEE-ASE] 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, 2016.

  • [SDM] Lin1, 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. (invited for poster presentation, paper acceptance rate < 25 %).

  • [AISTAT] 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. (invited for oral presentation, paper acceptance rate 6 %).

  • [IIE] 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.

  • [IEEE-EC] Yampikulsakul, N., Byon, E., Huang, S. and Sheng, S.W., “Condition Monitoring of Wind Power System with Non-Parametric Regression Analysis”, IEEE Transactions on Energy Conversion, Vol. 29(2), 288-299, 2014.

  • [PRL] Liu1, Y., Fabri, P., Zayas-Castro, J. and Huang, S., “Learning High-dimensional Networks with Nonlinear Interactions by a Novel Tree-Embedded Graphical Model”, Pattern Recognition Letters, Vol. 49(1), 207-213, 2014.

  • [APL] Wang, R., Huang, S., Li, J. and Chae, J., “Probing Thyroglobulin in Undiluted Human Serum based on Pattern Recognition and Competitive Adsorption of Proteins”, Applied Physics Letters, Vol. 105 (14), 143703-143705, 2014.

  • [IEEE-PAMI] Huang, S., Ye, J., Fleisher, A., Chen, K., Reiman, E., Wu, T., and Li, J., “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, 2013.

  • [IIE] Huang, S., Li, J., Chen, K., Wu, T., Ye, J., Wu, X., and Li, Y., “A Transfer Learning Approach for Network Modeling”, IIE Transactions, 44, 915-931, 2012.

  • [IIE] Huang, S., Li, J., Lamb, G., Schmitt, M., and Fowler, J., “Multi-data Fusion for Enterprise Quality Improvement by a Multilevel Latent Response Model”, IIE Transactions, 2011.

  • [Lab-on-a-chip] Choi, S., Huang, S., Li, J. and Chae, J., “Monitoring Protein Distributions based on Pattern Generated by Protein Adsorption Behavior in a Microfluidic Channel”, Lab-on-a-Chip, 11, 3681-3688, 2011.

  • [NIPS] 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. (invited for oral presentation, paper acceptance rate 4.8%).

  • [KDD] 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. (invited for poster presentation, paper acceptance rate 17.5%).

  • [IEEE-Reliability] Huang, S., Pan, R., and Li, J., “A Graphical Technique and Penalized Likelihood Method for Identifying and Estimating Infant Failures,” IEEE Transactions on Reliability, 59(4), 650-660, 2010.

  • [IIE] Li, J., and Huang, S., “Regression-based Process Monitoring with Consideration of Measurement Errors,” IIE Transactions, 42(2), 146-160, 2009.

  • [NIPS] 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. (invited for oral presentation, acceptance rate 8%).



Healthcare Applications (Disease Research/Healthcare Management)

  • [SR] Lin1, Y., Huang, S., Simon, G. and Liu, S., “Data-based Decision Rules to Personalize Depression Follow-up”, Scientific Report, 2018.

  • [JHIR] Ardywibowo, R., Huang, S., Gui, S., Xiao1, C., Cheng, Y., Liu, J. and Qian, X., “Switching-State Dynamical Modeling of Daily Behavioral Data”, Journal of Healthcare Informatics Research, 2018.

  • [AAAI-Abstract] Samareh1, A., Yan1, J., Wang, Z., Chang, X., Huang, S., “Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion”, In Proceedings of the 28-th AAAI Conference on Artificial Intelligence (AAAI), 2018.

  • [EURASIP] Huang, S., Zhou, J., Wang, Z., Ling, Q. and Shen, Y, “Biomedical informatics with Optimization and Machine Learning”, EURASIP Journal of Advances in Signal Processing, 2017.

  • [JBI] Huang, YJ., Meng1, Q., Evans, H., Lober, B., Cheng, Y., Qian, X., Liu, J. and Huang, S. “CHI: A Contemporaneous Health Index for Degenerative Disease Monitoring using Longitudinal Measurements”, Journal of Biomedical Informatics, 2017.

  • [MathBio] Lin1, Y., Huang, S., Simon, G. and Liu, S. “Analysis of Depression Trajectory Patterns using Collaborative Learning”, Mathematical Bioscience, 2016.

  • [EURASIP] Yan1, J., Su, Y., Zhou, XH. and Huang, S. “Heterogeneous Multimodal Biomarker Analysis for Alzheimer’s Disease via Bayesian Network”, EURASIP Journal of Advances in Signal Processing, 2016.

  • [ART] Grewal, R., Haghighi1, M., Huang, S., Smith, A., Cao, C., Lin, X., Lee, D., Teten, N., Pharm, H. and Selenica. “Identifying Biomarkers of Dementia Prevalent Among Amnestic MCI Ethnic Female Patients”, Alzheimer’s Research & Therapy, 2016.

  • [SR] Haghighi1, M., Johnson, S., Qian, X., Lynch, K., Vehik, K., Huang, S. and the TEDDY Study Group, “A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study”, Scientific Report, 2016.

  • [JBI] Ke, C., Yan1, J., Evans, H., Lober, B., Qian, X., Liu, J. and Huang, S. “Prognostics of Surgical Site Infections using Dynamic mHealth Data”, Journal of Biomedical Informatics, 2016.

  • [JACS] Sanger, P., van Ramshorst, G., Mercan, E., Huang, S., Hartzler, A., Armstrong, C., Lordon, R., Lober, W. and Evans, H., “A Prognostic Model of Surgical Site Infection Incorporating Daily Objective Wound Assessments using Machine Learning”, Journal of the American College of Surgeons, 2016.

  • [INFORMS TUTORIAL] 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.

  • [BIOKDD] Lu, M., Huang, S., Odegard, J., Speake, C., Huang, J.Z. and Qian, X. “EigenBiomarker: A Method for Composite Biomarker Detection with Applications in Type 1 Diabetes (T1D)”. Proc. 14th International Workshop on Data Mining in Bioinformatics (BIOKDD), Sydney, Australia, August 2015.

  • [MLCB] Lu,M., Huang, S., Odegard, J., Speake, C., Huang, J.Z. and Qian, X. “A Model for Scoring Candidate Biomarker Utility in High-Dimensional Datasets with Replicate Tests”. Proc. Workshops on Machine Learning in Computational Biology (MLCB) & Machine Learning in Systems Biology (MLSB) 2015, in conjunction with the Annual Conference on Neural Information Processing Systems (NIPS) 2015, Montreal, Canada, December 2015.

  • [PLOS ONE] Lin1, Y., Qian, X., Krischer, J., Vehik, K., Lee, H.S. and Huang, S., “A Rule-Based Prognostic Model for Type 1 Diabetes by Identifying and Synthesizing Baseline Profile Patterns”, PLOS ONE, 9(6):e91095, 2014.

  • [SURGERY] Hartney, M., Liu1, Y., Velanovich, V., Fabri, P., Marcet, J., Grieco, M., Huang, S. and Zayas-Castro, J., “Bounceback Branchpoints: Using Conditional Inference Trees to Analyze Readmissinos”, Surgery, Vol. 156 (4), 842-847, 2014.

  • [JAD] Haghighi1, M., Smith, A., Morgan, D., Brent, S. and Huang, S., “Identifying Cost-Effective Predictive Rules of Amyloid-Beta Level by Integrating Neuropsychological Tests and Plasma-based Markers”, Journal of Alzheimer’s Disease, Vol. 43(4), 1261-1270, 2014.

  • [NEUROIMAGE] Huang, S., Li, J., Sun, Li., Ye, J., Fleisher, A., Wu, T., Chen, K., and Reiman, E., “Learning Brain Connectivity of Alzheimer’s Disease by Exploratory Graphical Models,” NeuroImage, 50, 935-949, 2011.



Acknowledgment for Funding Support

  • National Science Foundation (CMMI-1505260, CMMI-1536398, CCF-1715027)
  • Juvenile Diabetes Research Foundation
  • NIH
  • DARPA
  • Byrd Alzheimer’s Institute
  • Royalty Research Foundation
  • Helmsley Foundation