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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.
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