Ph.D. Student
Computer Science

1148 Kelley Engineering Center
Corvallis, OR 97331-5501
 

Education

  • H.B.S. Computer Science, Oregon State University (2010)
  • H.B.S. Mathematical Science, Oregon State University (2010)
  • M.S. Computer Science, Oregon State University (2013)

Major Professor(s)

Research Interests

My current research is using quantile analysis methods for detecting unusual data regions. Percentile based models, such as quantile regression, provide a robust framework for modeling any desired percentile of a data distribution. This can be used to reduce the influence of data outliers, analyse extreme values, or identify multiple signals within a dataset. My research involves the creation of quantile scanning algorithms that can detect unusual data regions with respect to a given percentile of the distribution, as well as optimizations to make these algorithms scale-able to large datasets. Example applications include: detection of biodiversity hotspots, differentiation between expert and novice observations in citizen scientist data, detection of abnormally high (or low) regions with respect to unemployment, housing prices, crop yield, etc.

Publications

  • Moore, T. and Wong, W-K. (2018). An Efficient Quantile Spatial Scan Statistic for Finding Unusual Regions in Continuous Spatial Data with Covariates. To appear in UAI.
  • Kelling, S., Johnston, A., Hochachka, W. M., Iliff, M., Fink, D., Gerbracht, J., Lagoze, C., La Sorte, F. A., Moore, T., Wiggins, A., Wong, W-K., Wood, C. and Yu, J. (2015). Can Observation Skills of Citizen Scientists Be Estimated Using Species Accumulation Curves? PLoS ONE, 10(10): e0139600. doi:10.1371/journal.pone.0139600.
  • Moore, T. and Wong, W-K. (2015). Discovering Hotspots and Coldspots of Species Richness in eBird Data. In AAAI Workshop: Computational Sustainability.
  • Das, S., Moore, T., Wong, W-K., Stumpf, S., Oberst, I., McIntosh, K. and Burnett, M. (2013). End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression. Artificial Intelligence, 204:56-74.
  • Curran, W., Moore, T., Kulesza, T., Wong, W-K., Todorovic, S., Stumpf, S., White, R., and Burnett, M. (2012). Towards Recognizing "Cool": Can End Users Help Computer Vision Recognize Subjective Attributes of Objects in Images? Proceedings of the 2012 International Conference on Intelligent User Interfaces, (pp. 285-288), New York, NY: ACM Press.
  • Wong, W-K., Oberst, I., Das, S., Moore, T., Stumpf, S., McIntosh, K., and Burnett, M. (2011). End-User Feature Labeling via Locally Weighted Logistic Regression. Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, (pp. 1575-1578).
  • Wong, W-K., Oberst, I., Das, S., Moore, T., Stumpf, S., McIntosh, K., and Burnett, M. (2011). End-User Feature Labeling: A Locally-Weighted Regression Approach. ACM International Conference on Intelligent User Interfaces, (pp. 115-124), New York, NY: ACM Press. Best Paper Nomination at IUI 2011.
  • Kulesza, T., Stumpf, S., Burnett, M., Wong, W-K., Riche, Y., Moore, T., Oberst, I., Shinsel, A., and McIntosh, K. Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs. (2010). In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing 2010, (pp. 41-48).