Computer Science Colloquium

Probabilistic models are useful for modeling non-deterministic data generation processes. Examples of these can be found in genetic domains representing gene expression interactions, socio-economic domains representing stock market prices influences by current events, and many others. The greatest problem in modeling such processes is determining the structure of the model. In my talk I will present work I have done at Carnegie Mellon towards inferring the structure of a specific class of models called Bayesian networks (BNs). I will present the GS ("grow-shrink") algorithm which uses conditional independence tests to determine the BN structure. I will also present a statistical independence test that shows progress towards a conditional independence test for domains with continuous variables, a problem currently unsolved in its generality. I will also show some results of an application that uses Bayesian network models to answer count queries from very large databases. My approach features constant time in the size of the database, a small space overhead, and linear preprocessing time. Moreover it is easily parallelizable and can be readily used for data-mining purposes, at no extra cost.

Dimitris Margaritis is a Ph.D. student at the department of Computer Science at Carnegie Mellon University. He holds a BS in Physics from the University of Athens, Greece and a MS in Computer Science from SUNY Stony Brook. In the past he has worked on a diverse number of projects such as multi-agent modeling, computational DNA sequencing by hybridization, image database retrieval, and 3D computer vision for robots. More recently his work is on developing a multiresolution statistical independence test for continuous variables, and implementing fast, approximate querying of count information from very large databases, using Bayesian network techniques. His current interests are in machine learning, probability theory, decision theory, and statistics, with a focus on Bayesian network structure induction. He is particularly interested in innovative applications of machine learning techniques to difficult problems. Mr. Margaritis is a member of AAAI, the Hellenic Artificial Intelligence Society, and Sigma Xi. on human motion.