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Weng-Keen Wong

Associate Professor
School of Electrical Engineering and Computer Science
Oregon State University

Office: Kelley 2075
Email: my last name at eecs dot oregonstate dot edu
Mailing Address:: School of Electrical Engineering and Computer Science
Oregon State University
1148 Kelley Engineering Center
Corvallis, OR 97331-5501

Note: The best way to reach me is by email, not by phone.

At the Arc de Triomphe, Paris, France

[ Professional | Research | Personal | Lab | Notes]

Research

Much of my research involves investigating novel machine learning / data mining problems that arise in other fields. Here are my main research interests:

I. Interactive Machine Learning

End-user debugging of Machine Learning Systems (with Margaret Burnett, Simone Stumpf, and Alex Groce

II. Ecosystem informatics

Species Distribution Mapping (with Tom Dietterich, Matt Betts, Julia Jones)
Species distribution mapping involves understanding species-habitat relationships. Environmental features such as temperature, precipitation, vegetation, land use, etc. determine if a site is viable habitat for a species. Species distribution mapping can be viewed as a supervised learning problem. However, a crucial predictive feature is the presence/absence of other species. We are developing novel multi-label classification algorithms for the prediction of multiple species simultaneously.
Range Shift (with Matt Betts and Julia Jones)
Several papers have indicated that species react in different ways to climate and land use change. Two common reactions include shifting their range and changing their community structure. We are developing machine learning algorithms to detect range shift and community change in species distribution data.

III. Time Series classification

Activity recognition and Energy Expenditure Prediction through Accelerometry (with Stewart Trost)
I am currently working with Stewart Trost (Health and Human Sciences) on algorithms for activity recognition from accelerometer data. This research involves classifying entire time series into activity classes and predicting energy expenditure.

IV. Anomaly Detection

Rare category detection
A challenging problem in anomaly detection is to discover interesting anomalies and not just statistically significant ones. Rare category detection is a human-in-the-loop process that bears a resemblance to active learning. In rare category detection, the machine learning algorithm presents interesting categories of anomalies to the user for labeling.
Surveilance
In the past, I worked on the early detection of disease outbreaks through the analysis of health-care data.

Conference Deadlines

ICCV 2013 (Dec 3-6, 2013, Sydney, Australia): Abstract submission April 8, 2013, Full paper April 12, 2013.
NIPS 2013 (Dec 5-8, 2013, Lake Tahoe, NV): Full paper submission: May 31, 2013 16:00 PDT.
ICDM 2013 (Dec 8-11, 2013, Dallas, TX): Full paper submission: June 21, 2013.