Conference on High-Dimensional Statistics

High-Dimensional Statistics has grown out of modern research activities in diverse fields such as science, technology, and business, aided by powerful computing. It encompasses several emerging fields in statistics such as high-dimensional inference, dimension reduction, data mining, machine learning, and bioinformatics.

Diane Lambert

Title of Talk: Statistics At Google Scale

Abstract: Google is a big statistics engine that collects, organizes, summarizes and analyzes data to provide users access to information anywhere and at any time. At its core lie measurement, experimentation and learning followed by implementation, and more measurement, experimentation and learning — almost all of which is automated and self-monitoring. This talk will show how Google combines data science, statistical principles and huge amounts of data to improve search and ads for users, advertisers and publishers.

Brief Bio:

Diane Lambert is a statistician who has made a career out of learning how to wrestle with, and sometimes tame, data. She started in academia (she was tenured by the statistics department at CMU), moved to Bell Labs where she became a Bell Labs Fellow and department chair, and then left for Google where she is now a research scientist, focused on data science and its applications, ranging from monitoring a gigantic robotic tape drive system to measuring ad effectiveness and understanding users’ interactions with Google apps. Recently, she served on a National Academy of Sciences panel on massive data and the Committee of Visitors for the Division of Mathematical Sciences at the National Science Foundation.