Dr. Alexander J. Smola

"Scalable Machine Learning for the user"

(Vortrag im Rahmen der "Distinguished Lecture Series" des "Max Planck Instituts für Software-Systeme")

Scalable content personalization and profiling is a key tool for the internet. In this talk I will illustrate based on three problems how this can be achieved. More specifically I will show how hashing can be used to deal with compactly representing enormous amounts of parameters, how distributed latent variable inference can be used for user profiling, and how session modeling provides an attractive alternative to ranking.

Bio: Alex Smola received the Master's degree in Physics at the University of Technology Munich in 1996, and the Doctoral Degree in computer science at the University of Technology Berlin in 1998. Until 1999 he was a researcher at the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer Geselschaft). After that, he worked as a Researcher and Group Leader at the Research School for Information Sciences and Engineering of the Australian National University. From 2004 onwards he worked as a Senior Principal Researcher and Program Leader at the Statistical Machine Learning Program at NICTA. Since 2008, he has been a Principal Research Scientist at Yahoo! Research in Santa Clara, CA, USA.