[Research] Fwd: [Eng-pit] Google Seminar 8/17 @ 10:00a - David Jensen - Learning causal dependencies in networks

[Andrew W. Moore]

Dear Auton Labbers,

David (a long time friend of Auton) is visiting this Friday. Let me know if
in addition to his talk you'd like time to meet with him.

Thanks,

Andrew


---------- Forwarded message ----------
From: Cathy Serventi <serventi at google.com>
Date: Aug 14, 2007 7:24 PM
Subject: [Eng-pit] Google Seminar 8/17 @ 10:00a - David Jensen - Learning
causal dependencies in networks
To:

please forward as appropriate

Please join us for a Google Seminar this Friday.

Where: Google Pittsburgh, CIC Building, Lower Level, CMU
Day: Friday, 8/17
Time: 10:00am

Google Seminars are open to the public.  Refreshments will be served.


Learning causal dependencies in networks
David Jensen
Associate Professor of Computer Science
Director, Knowledge Discovery Laboratory
University of Massachusetts Amherst

Over the past decade, machine learning researchers have developed techniques
to estimate the joint distribution of a set of variables that span multiple
related entities.  These methods, often grouped under the rubric "relational
learning", include probabilistic relational models, relational Markov
networks, and relational dependency networks.  These techniques build on
work in artificial intelligence, statistics, databases, graph theory, and
social network analysis, and they are profoundly expanding the phenomena
that we can understand and predict.  However, new frontiers await.

In this talk, I will briefly survey some recent work in learning
probabilistic models of relational data, and discuss several
applications of these techniques, including fraud detection in the U.S.
securities industry.  I will argue that current techniques are capable of
learning only a subset of the knowledge needed by practitioners in these
domains, and that informing effective action often requires a causal model.
I will examine the open question of whether relational representations make
the problem of learning causal models easier or harder, and present some
reasons for optimism that relational representations may be able to greatly
improve our ability to learn such models.

David Jensen is Associate Professor of Computer Science and Director of the
Knowledge Discovery Laboratory at the University of Massachusetts Amherst.
He received his doctorate from Washington University in 1992.  From 1991 to
1995, he served as an analyst with the Office of Technology Assessment, an
agency of the United States Congress.  His research focuses on machine
learning and knowledge discovery in relational data, with applications to
social network analysis, web mining, and fraud detection.  He serves on the
program committees of the International Conference on Machine Learning and
the International Conference on Knowledge Discovery and Data Mining, and he
serves on the ACM SIGKDD Executive Committee.  He is a member of the
2006-2007 Defense Science Study Group.

If you have questions or would like to be added to the Seminar mailing list
please contact me:
-- 
Cathy Serventi
Google Pittsburgh Engineering Office
Office: (412) 297-5400
serventi at google.com


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