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AIC Seminar Series

Improving Web Information Finding via Machine Learning

Mehran SahamiGoogle and Stanford University[Home Page]

Notice:  hosted by Sugato Basu

Date:  2005-12-15 at 16:00

Location:  EJ228  (Directions)

   Abstract

Web search is one of the most important applications used on the Internet. It also poses many interesting opportunities to apply machine learning and make use of massive amounts of data. In order to better help people find relevant information in a growing sea of data, we discuss various automated learning techniques that can be harnessed to sift, organize, and present relevant information to users. In this talk, we provide a brief background on information retrieval, and then look at some of the challenges faced in searching the Web. Specifically, we examine applications of machine learning to improve information retrieval in a variety of contexts, topical inference for queries, and record linkage. We show how these tasks are directly related to the overarching goal of improving various aspects of search on the web.

   Bio for Mehran Sahami

Mehran Sahami is a Senior Research Scientist at Google and also a Lecturer in the Computer Science Department at Stanford University. At Google, Mehran conducts research in machine learning technologies to help improve information access. Prior to Google, Mehran was involved in a number of commercial and research machine learning projects, including managing the data mining and advanced technologies groups at Epiphany, and working on research projects at Xerox PARC and Microsoft Research. He also once had the great pleasure of being a summer intern at SRI. He received his BS, MS, and PhD in Computer Science from Stanford, and evidently still has issues with regard to really leaving there.

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