Statistical relational learning is a newly emerging area of research
that combines statistical modeling with relational representations.
Several approaches have been proposed including Probabilistic
Relational Models (PRMs), Bayesian Logic Programs, Stochastic Logic
Programs to name a few.  In this talk I will review recent work on
PRMs and describe the development of techniques for automatically
inducing PRMs directly from structured data stored in a relational or
object-oriented database.  I will describe some of the ways in which
uncertainty over the relational structure can be modeled and describe
learning algorithms for the models.  As we go along, I'll present
experimental results in several domains, including a biological domain
describing tuberculosis epidemiology, a database of scientific paper
author and citation information, and Web data. Finally, if time
permits, I will present an application of these techniques to the task
of selectivity estimation for database query optimization.

bio:

Lise Getoor recently joined the University of Maryland, College Park
as an assistant professor in the computer science department.  She
received her PhD from Stanford University in December, 2001.  Her
research interests include probabilistic models, machine learning and
data management.  She has published papers on a variety of topics
including learning probabilistic models, utility elicitation, on-line
scheduling, query selectivity estimation, constraint-based planning
and machine learning.  Before pursuing her PhD at Stanford, she worked
at NASA-Ames Research Center as a research associate.  She received
her M.S. in Computer Science from UC Berkeley and her B.S. in Computer
Science from UC Santa Barbara.