Research Interests

We are interested in developing data mining and machine learning algorithms with theoretical properties to better discover actionable patterns from large-scale, networked, dynamic and sparse data. Our research is directly motivated by, and contributes to, applications in social informatics, health informatics and information security. Our work has been featured in Various News Media, such as ACM TechNews, New Scientist, Defense One, Economic Times. Here is my Google Scholar. Specifically, we are interested in the following topics:

  • Automated Machine Learning
  • Explainable Artificial Intelligence
  • Network Analytics
  • Health Data Science
  • Anomaly Detection

Ongoing Research Projects

  • III: Medium: Collaborative Research: Towards Effective Interpretation of Deep Learning: Prediction, Representation, Modeling and Utilization. National Science Foundation. PI.
  • S&AS: INT: Autonomous Experimentation Platform for Accelerating Manufacturing of Advanced Materials. National Science Foundation. Co-PI.
  • Search in Card Games with Incomplete Information. JJWorld Network Technology. PI.
  • SaTC: CORE: Small: Adversarial Learning via Modeling Interpretation. National Science Foundation. PI.
  • CAREER: Human-Centric Big Network Embedding. National Science Foundation. Sole PI.
  • Anomaly Detection on Large-scale and Streaming Data. Apple Inc. PI.
  • Large Scale Data Analytics for Air Conditioning Systems. Ingersoll Rand. Co-PI.
  • RAPID: Assessment of Risks and Vulnerability in Coupled Human-Physical Networks of Houston's Flood Protection, Emergency Response, and Transportation Infrastructure in Harvey. National Science Foundation. Co-PI.
  • RAPID: Houston in Hurricane Harvey (H3): Establishing Disaster System-of-Systems Requirements for Network-Centric and Data-Enriched Preparedness and Response. National Science Foundation. Co-PI.
  • III: Small: Collaborative Research: A General Feature Learning Framework for Dynamic Attributed Networks. National Science Foundation. Lead PI.
  • Transforming Deep Learning to Harness the Interpretability of Shallow Models: An Interactive End-to-End System. DOD-Advanced Research Projects Agency. PI.
  • Automatic Composition of Complex Pipelines via End-to-End Learning and User Interaction. DOD-Advanced Research Projects Agency. PI.
  • CRII: III: Novel Embedding Algorithms for Large-scale and Complex Attributed Networks. National Science Foundation. Sole PI.
  • HELIOS: Accelerated Recovery of Evolving Spatial-Temporal Dynamics. DOD-Advanced Research Projects Agency. Co-PI.
  • Deep Gesture Recognition. King City Technology Limited. Sole PI.
  • Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS UP). National Science Foundation. Senior Personnel.

Ongoing Educational Projects

  • Big Data Analytics Solutions for Smart and Resilient Urban Infrastructure Systems. AggiE-Challenge program, College of Engineering, Texas A&M University. PI.
Acknowledgments: Our lab would like to thank generous support from DARPA, NSF, Apple, Alibaba, Ingersoll Rand, KC Sence, UnitedHealthcare, Nvidia, the Texas A&M College of Engineering, and Texas A&M. Thank you!

Media Coverage