Pavel Berkhin
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Description

Dr. Pavel Berkhin is a Sr. Director of Data Mining and Research. He leads the efforts to better analyze and utilize Yahoo! user data. Data Mining and Research group (DMR), part of Strategic Data Solutions, is involved in a broad range of Yahoo! data analysis, from Yahoo! Data Mining platform to anomaly detection, from behavioral targeting to modeling for search advertisement, from studies of user adoption patterns to keyword set expansion. Pavel also worked on theoretical challenges facing Yahoo! Search including link-based spam detection, personalization, trust networks, and new ways for PageRank computing.

Prior to joining Yahoo!, Pavel kept positions of a Chief Scientist with Accrue Software, Inc., a web analysis company, and a Chief Scientist of Neo Vista, Inc., a provider of industrial data mining software for banking, insurance, and retail. He is known for his work in exploratory data analyses and informational co-clustering. Pavel also worked in the area of numerical analysis leading development of industrial computational libraries at National Instruments, Inc. He served on numerous program committees for a variety of scientific conferences.

Pavel Berkhin earned his Ph.D. in mathematics from Institute of Mathematics, Novosibirsk, USSR, under supervision of Professor Sergey Sobolev. His research interests were in partial differential equations. His mathematical publications are related to boundary value problems, pseudo-differential operators, theory of diffraction, and probability theory.


Lectures:

interview
flag Interview with Pavel Berkhin
as interviewee at  13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007,
9677 views
  lecture
flag Successes, Failures and Learning From Them
as author at  13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007,
6388 views
opening
flag Introduction to the KDD07 Conference
as author at  13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007,
6403 views