Collaborative Support Faculty

Shen-Shyang Ho

Assistant Professor, Computer Science


BS (Mathematics with Computational Science), National University of Singapore
PhD (Computer Science), George Mason University
Postdoctoral, California Institute of Technology and NASA Jet Propulsion Laboratory

Research Expertise

Data Mining | Artificial Intelligence | Machine Learning | Pattern Recognition

My current research interests are: spatiotemporal data mining, privacy issues in data mining, machine learning on network/graph data, learning analytics. My research on spatiotemporal data mining focuses on (i) overcoming data management issues using distributed array-based database, (ii) analytic algorithms and tasks, (iii) data representation using networks and tree structures, (iv) privacy issues, and (v) application developments. My projects and investigations are both research-driven and application-driven. The application-driven investigations utilize real-world data such as mobile data from smartphones, crowdsourced sensor data collected from smartphone, factory sensor data, text data (from internet), image data, and satellite data.

I am also interested in (i) developing new methods for discovering interesting or useful events (and patterns) in heterogeneous data from multiple data sources, (ii) effective machine learning using very few labeled examples, and (iii) applying data analytics and machine learning techniques to support technological-enabled learning. 


Association for Computing Machinery
Institute of Electrical and Electronics Engineers

Recent Publications

  1. S.-S. Ho, P. Dai, F. Rudzicz, Manifold Learning for Multivariate Variable-Length Sequences With an Application to Similarity Search, IEEE Transactions on Neural Networks and Learning System, Vol. 27, No. 6, pp. 1333-1344, 2016.
  2. P. H. Chen and S.-S. Ho, Is overfeat useful for image-based surface defect classification tasks?, IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, pp. 749-753, 2016.
  3. J. Cherian, J. Luo, H. Guo, S.-S. Ho and R. Wisbrun, ParkGauge: Gauging the Occupancy of Parking Garages with Crowdsensed Parking Characteristics, 17th IEEE International Conference on Mobile Data Management (MDM), Porto, pp. 92-101, 2016.
  4. V. Balasubramanian, S.-S. Ho, and V. Vovk, Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications. Morgan Kaufmann, 2014.

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