Weng-Keen Wong

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 Forth Rail Bridge (Edinburgh, Scotland)

News

(Sept 23, 2020) I'm the OSU Site Director for the Pervasive Personalized Intelligence Center, which is a National Science Foundation funded Industry-University Collaborative Research Center. If you are from industry and interested in collaborating with us, please send me an email!

(Sept 23, 2020) I'm now a Full Professor (quite a contrast to the days of being a hungry grad student)

(Oct 1, 2018) I am back at Oregon State University after serving as a Program Director in CISE/IIS/RI at the National Science Foundation.

Research

Anomaly Detection

• (General) Anomaly Detection
  Anomaly detection is a very vaguely defined area of machine learning / data mining but at a high-level, it involves discovering unusual but meaningful data instances. I am interested in anomaly detection for structured data, explaining anomalies and harnessing user feedback to improve anomaly detection.

• Open set recognition
  Open set recognition requires a classifier to accurately recognize classes seen during training and also detect when new classes that were not seen during training. This problem is closely related to out-of-distribution detection and plays an important role in making machine learning robust to real world problems.

• 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 representative instances of each class to a user for labeling so that the user can discover as many classes as possible given a budget of queries.

Explainable AI (XAI)

• Explaining supervised learning
  I am interested in explaining deep learning classifiers, with a focus on understanding what is going in latent representation layers. In addition, I am interested in developing techniques to produce counterfactual explanations to help users of machine learning algorithms understand thebehavior of their algorithm.

Computational Sustainability

• Species Distribution Modeling
  Species distribution mapping involves predicting the occurrence of a species from a set of environmental features (such as temperature, precipitation, vegetation, land use, etc.) Species distribution modeling can be viewed as a supervised learning problem, but a large number of real-world problems make it much more challenging. One major issue I am working on involves dealing with imperfect detection of species, which is a problem encountered in large biodiversity citizen science datasets like eBird .

Temporal, Spatial and Spatio-temporal Machine Learning

• My research in this area includes weakly-supervised activity recognition from time series data, spatial anomaly detection and discovering latent spatio-temporal patterns.

Inverse Mandalorian Monte Carlo Markov Chain with Groguian Dynamics

• I don't really do research in this non-existent area but this paragraph is here to weed out graduate applicants that blindly cut and paste from my research interests.

Links

• Course proposal process
• Sample syllabus
• EECS Course Proposal Form
• Machine Learning Reading Group (Winter 2014)
• MLJ Special Issue on Event Detection
• AI Seminar
• Kelley room reservations
• OSU EECS Appointment database
• Confluence
• MLRG
• OSU Travel
• Tips on Writing Research Papers
• Datasets available on the web

Conference Deadlines

• IJCAI 2021(Aug 21-26, 2021), Montreal, Canada: Abstract submission Jan 13, 2021, Full paper Jan 20, 2021.
• ICML 2021(July 18 - 24, 2021), Virtual: Abstract submission Jan 28, 2021, Full paper Feb 4, 2021.
• UAI 2021(July 27 - 29, 2021), Virtual: Full paper Feb 19, 2021.
• KDD 2021(August 14 - 18, 2021), Singapore: Full paper Feb 8, 2021.