Current undergraduate students

Speaker: Georgia Kastidou

In this paper, we present a model for delivering personalized ads to users while they are watching TV shows. Our approach is to model user preferences, based on characterizing not only the keywords of primary interest but also the relative weighting of those keywords.

Speaker: Allan Caine

In this talk, we will present the 2D Stockwell transform (2D s-transform) and its application to image processing.

Speaker: Jie Zhang

In multiagent systems populated by self-interested agents, consumer agents would benefit by modeling the reputation of provider agents, in order to make effective decisions about which agents to trust. One method for representing reputation is to ask other agents in the system (called advisor agents) to provide ratings of the provider agents.

Speaker: Derek Koehler (Cognitive Psychology)

A stock market simulation tested whether known biases in probability judgment are attenuated in an asset pricing setting.

Speaker: Richard Mann

Classical methods for measuring image motion by computer have concentrated on the cases of optical flow in which the motion field is continuous, or layered motion in which the motion field is piecewise continuous. Here we introduce a third natural category which we call optical snow.

Speaker: Jane Morris

Most research on automated text understanding, including applications in information retrieval and text summarization, does not consider the potential subjectivity of human interpretation of text. In particular, the subjectivity of the linguistic theory of lexical cohesion that has been used in these applications has not been examined empirically.

Speaker: Zhenmei Gu

In information extraction (IE), training statistical models like HMMs (hidden Markov models) usually requires a considerably large set of labeled data. Such a requirement may not be easily met in a practical IE application. In this talk, we investigate how to adapt a fully supervised IE learner to a semi-supervised one so that the learner is able to make use of unlabeled data to help train a more robust model from very limited labeled data.

Speaker: Chris Eliasmith

I present a cognitive model, dubbed BioSLIE, that integrates and extends recent advances in:

• distributed, structure sensitive representation
• neurocomputational modeling
• our understanding of the neuroanatomy of linguistic inference

Speaker: Dana Wilkinson

Given a collection of data points annotated with action labels (obtained, for example, from a robot mounted with a web camera or a laser range finder) we wish to learn a representation of the data which can be used as a map (for planning, etc.).

Speaker: Shai Ben-David

Support Vector machines (SVM's) is one of the most useful and widely applicable machine learning techniques. Each concrete application of SVMs depends on a successful choice of a "kernel matrix". So far, most of the work in this area has focused on developing a variety of kernels and efficient algorithms for employing SVMs with these kernels. Relatively little research attention has been given to the question of how to pick a suitable kernel for any particular learning task at hand.

In this work, we analyze exactly that issue.