Shuai Huang

Machine Learning, Healthcare, and Engineering


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)).


Example Projects

Collaborative Construction of Models for Networked Systems by Human-AI Collaboration


Mitigate Data Disparities (such as minority bias, aggregation bias) by Collaborative Learning


Improve the Interpretability and Transparency of Risk Prediction of Type 1 Diabetes by Rule-based Methods


Understand the Bias and Uncertainties in Human Mobility Data

Check out our project website


Modeling of User Choice Behavior and Interactions with Reward-offering AI Systems


Other examples from my previous website

  • Brain connectivity modeling using Neuroimaging data (link)
  • Towards mechanistic understanding of type 1 diabetes (link)
  • Smart monitoring of complex diseases (link)
  • Integration with mHealth technology (link)

Acknowledgment for Funding Support

  • National Science Foundation (CMMI-1505260, CMMI-1536398, CMMI-1824623, CCF-1715027, CIS-2114260)
  • Juvenile Diabetes Research Foundation
  • NIH
  • Byrd Alzheimer’s Institute
  • Royalty Research Foundation
  • Helmsley Foundation