|October 27, 4:30pm||Muthu Muthukrishnan, Rutgers CS||Text Streaming|
|POSTPONED||Diane Lambert, Bell Labs||TBA|
|December 1, 4:30pm||Rick Mammone, CAIP||Rutgers Warning and Indication System Engineering Laboratory-The WISE Lab|
|December 18, 2:00pm||Michael Littman, Rutgers CS||Sequential Decision Making Algorithms in Filtering|
In the past few years we have developed new algorithmic methods for dealing with fast streams of mostly numerical, categorical or hierarchical data. These were motivated by IP network traffic logs. Emerging applications now involve streams of text data. How to apply the extant work to these text streams? I will explore this question, as interactively as possible
We have been exploring the possibility of applying algorithms from machine learning and sequential decision making to text filtering. This is preliminary work and we welcome feedback from the the "monitoring message stream" community. To that end, I will provide background on the Markov decision process (MDP) and partially observable MDP models and will describe our current efforts in using these models for filtering. Our motivation is to automatically make filtering decisions that trade off exploration and exploitation. This is joint work with Peng Song, David Madigan, and Paul Kantor
The mission of the WISE lab is to develop and improve data processing for a variety of situational awareness systems. Data from various sensors such as: nuclear, biological, chemical, surveillance sensors as well as text messages generated by observers in the field are transported to a central server. The challenge is to identify the various sources of the data. For example if the data pertains to a person then the system should automatically identify the person. If the data comes from a material then system should identify the material, for example: materials used for explosives, nuclear, biological and chemical weapons. In addition the system should indicate the location of the identified person or material. The WISE lab has developed digital signal processing and pattern recognition algorithms to translate the raw sensor data into these important classes. Applications of the algorithms in the area of biometrics, chemometrics, and geometrics will be presented. In addition a new Short Messaging Service (SMS) warning system will be described. The WISE Lab is interested in exploring the question of how text analysis can help in the overall warning and indicators systems. For more information about the WISE Lab see: www.caip.rutgers.edu/wiselab.