Thursday, September 15, 2005
4:15 pm
B17 Upson Hall

Computer Science
Fall 2005

Daphne Koller
Stanford University

Probabilistic Models for Complex Domains:
Cells, Bodies, and Webpages

Many domains in the real world are richly structured, containing a diverse set of objects, related to each other in a variety of ways. For example, a living cell contains a rich network of interacting genes, that come together to perform key functions. A robot scan of a physical environment contains diverse objects such as people, vehicles, trees, or buildings, each of which might itself be a structured object. And a website contains a set of interlinked webpages, representing diverse kinds of entities. This talk describes a rich language based on probabilistic graphical models, which allows us to model domains such as these. We show how to learn such models from data generated from the domain, and how to use the learned model both to gain a better understanding of the principles underlying these domains, and to allow us to analyze a new data set from these domains in order to recognize the entities in it and the relationships between them. In particular, I will describe applications of this framework to various tasks, including: recognizing regulatory and protein interactions in a cell from diverse types of genomic data; segmenting and recognizing objects in robot laser range scan data; and identifying the set of entities in a structured website and the relationships between them.

Biographical sketch

Daphne Koller received her BSc and MSc degrees from the Hebrew University of Jerusalem, Israel, and her PhD from Stanford University in 1993. After a two-year postdoc at Berkeley, she returned to Stanford, where she is now an Associate Professor in the Computer Science Department. Her main research interest is in creating large-scale systems that reason and act under uncertainty, using techniques from probability theory, decision theory and economics. Daphne Koller is the author of over 100 refereed publications, which have appeared in venues spanning Science, Nature Genetics, the Journal of Games and Economic Behavior, and a variety of conferences and journals in AI and Computer Science. She was the co-chair of the UAI 2001 conference, and has served on numerous program committees and as associate editor of the Journal of Artificial Intelligence Research and of the Machine Learning Journal. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, and the MacArthur Foundation Fellowship in 2004.