Title:

Graphical Models for the Internet

Abstract:

In this talk I will present algorithms for performing large scale inference using Latent Dirichlet Allocation and a novel Cluster-Topic model to estimate user preferences and to group stories into coherent, topically consistent storylines. I will discuss both the statistical modeling challenges involved and the very large scale implementation of such models which allows us to perform estimation on over 50 million users on a Hadoop cluster.

Bio:

I studied physics in Munich at the University of Technology, Munich, at the Universita degli Studi di Pavia and at AT&T Research in Holmdel. During this time I was at the Maximilianeum München and the Collegio Ghislieri in Pavia. In 1996 I received the Master degree at the University of Technology, Munich and in 1998 the Doctoral Degree in computer science at the University of Technology Berlin. Until 1999 I was a researcher at the IDA Group of the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer Geselschaft). After that, I worked as a Researcher and Group Leader at the Research School for Information Sciences and Engineering of the Australian National University. >From 2004 onwards I worked as a Senior Principal Researcher and Program Leader at the Statistical Machine Learning Program at NICTA.

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