| Welcome and Introduction (9:00-9:20) [slides] |
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| Session 1 (9:20-10:50) |
- 9:20-10:00
- Interactive Event Detection in Audio and Video [slides]
- Rahul Sukthankar
- 10:00 - 10:25
- Framework for Anomalous Change Detection [slides]
- James Theiler, Simon Perkins
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- 10:25-10:50
- Shape Outlier Detection Using Pose Preserving Dynamic Shape Models [slides]
Chan-Su Lee, Ahmed Elgammal
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| Coffee Break (10:50-11:20) |
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| Session 2 (11:20-12:40) |
- 11:20-12:00
- Detection of Stepping-Stones: Algorithms and Confidence Bounds [slides]
- Shobha Venkataraman
- 12:00-12:20
- Distributed Probabilistic Inference for Detection of Weak Network Anomalies [slides]
- Denver Dash
- 12:00-12:20
- Learning Sequential Models for Detecting Anomalous Protocol Usage [slides]
- Lloyd Greenwald
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| Lunch (12:40-14:05) |
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| Session 3 (14:05-15:45) |
- 14:05-14:45
- Forecast, Detect, Intervene: Anomaly Detection for Time Series [slides]
- Deepak Agarwal
- 14:45-15:25
- Bayesian Biosurveillance [slides]
- Greg Cooper, University of Pittsburgh
- 15:25-15:45
- A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks [slides]
- Thomas Lotze, Galit Shmueli, Sean Murphy, Howard Burkom
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| Coffee Break (15:45-16:15) |
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| Session 4 (16:15-17:35) |
- 16:15-16:45
- Towards a Learning Traffic Incident Detection System [slides]
- Tomas Singliar, Milos Hauskrecht
- 16:45-17:05
- Bayesian Anomaly Detection [slides]
- Tim Menzies, David Allen
- 17:05-17:35
- Discussion Panel [slides]
- Terran Lane
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