Shen-Shyang Ho

Dr. Ho received his B.S.(Hons) from National University of Singapore, and both his M.S and Ph.D. from George Mason University. His research interests are Spatiotemporal Data Mining, Machine Learning, Artificial Intelligence, Pattern Recognition Data Science, and Privacy Issues in Data Mining.

He has taught the following courses at Rowan: Discrete Mathematics, Engineering Mathematics, Biostatistics, Reasoning with Objects, Artificial Intelligence, Advanced Data Management (Spatial Database), Introduction to Object Oriented Programming, Data Mining.

Education:

• 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: transfer learning, one-shot learning, computational creativity, spatiotemporal data mining, privacy issues in data mining, machine learning on network/graph data. 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 using smartphone, factory sensor data, text data (from internet), audio data, image data, and satellite data.

Member of:

• Association for Computing Machinery
  Institute of Electrical and Electronics Engineers

Honors and Awards:

• NASA Postdoctoral Fellowship, 2007-2009.

Recent Academic Projects:

• 2017 SURP projects: “Histogram-based Conformal Set Predictor with Application to Trajectory-based Object Similarity Search” and “A Knowledge Transfer Framework for Computational Creativity with Application to Music Generation”

Recent Publications:

• Zhao J, Ho S-S (2017). Structural knowledge transfer for learning Sum-Product Networks. Knowledge-Based Systems, 122:159-166.
• Chai WH, Ho S-S, Goh CK, Chia LT, Quek HC (2017). A fast sparse reconstruction approach for high resolution image-based object surface anomaly detection. Fifteenth IAPR International Conference on Machine Vision Applications (MVA), 13-16.
• Ho S-S, Dai P, Rudzicz F (2016) Manifold Learning for Multivariate Variable-Length Sequences With an Application to Similarity Search, IEEE Transactions on Neural Networks and Learning System. 27:1333-1344.
• Chen PH, Ho S-S (2016) Is overfeat useful for image-based surface defect classification tasks? IEEE International Conference on Image Processing (ICIP), pp. 749-753.
• Cherian J, Luo J, Guo H, Ho S-S, Wisbrun R (2016) ParkGauge: Gauging the Occupancy of Parking Garages with Crowdsensed Parking Characteristics, 17th IEEE International Conference on Mobile Data Management (MDM), Porto, pp. 92-101.