Download Shen-Shyang Ho

Shen-Shyang Ho
Assistant Professor
Computer Science
[email protected]
https://sites.google.com/site/shenshyang/
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: 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.
Member of:
Association for Computing Machinery (www.acm.org)
Institute of Electrical and Electronics Engineers (www.ieee.org)
Recent Publications:
Ho S-S, Dai P, Rudzicz F. 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.
Chen PH, Ho S-S. Is overfeat useful for image-based surface defect classification tasks? IEEE International Conference
on Image Processing (ICIP), pp. 749-753, 2016.
Cherian J, Luo J, Guo H, Ho S-S, Wisbrun R. 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.
Balasubramanian V, Ho S-S, Vovk V. Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and
Applications. Morgan Kaufmann, 2014.
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