Machine Learning - Modern Times
Today's large-scale data sets and streams present machine learning with unprecedented challenges in keeping up with the deluge of records to be processed and scored. In this talk, I will discuss some of the algorithmic advances and implementation solutions we have designed at Google to effectively and efficiently handle these problems. The talk will discuss machine learning examples from a number of Google applications including Gmail, Image, and Product Search.
Corinna Cortes is the Head of Google Research, NY, where she is working on a broad range of theoretical and applied large-scale machine learning problems. Prior to Google, Corinna spent more than ten years at AT&T Labs - Research, formerly AT&T Bell Labs, where she held a distinguished research position. Corinna's research work is well-known in particular for her contributions to the theoretical foundations of support vector machines (SVMs) for which she jointly with Vladimir Vapnik received the 2008 Paris Kanellakis Theory and Practice Award, and for her work on data-mining in very large data sets for which she was awarded the AT&T Science and Technology Medal in the year 2000. Corinna received her MS degree in Physics from the Niels Bohr Institute in Copenhagen and joined AT&T Bell Labs as a researcher in 1989. She received her Ph.D. in computer science from the University of Rochester in 1993. Corinna is also a competitive runner, placing third in the More Marathon in New York City in 2005, and a mother of two.