Moses Charikar: 2016 Plenary Session

11:15am

Designing algorithms for efficient processing of massive data sets poses unique challenges. In this talk I will discuss algorithmic paradigms that have been developed to efficiently process data sets much larger than available memory. I will give a glimpse of some of these powerful tools: streaming algorithms and sketching methods that produce compact data structures, dimension reduction methods that preserve geometric structure, efficient algorithms for numerical linear algebra, graph sparsification methods, as well as impossibility results for these techniques.

Moses Charikar is a professor of Computer Science at Stanford University since Fall 2015. He obtained his PhD from Stanford in 2000, spent a year in the research group at Google, and was on the faculty at Princeton from 2001-2015. He is broadly interested in the design and analysis of algorithms with an emphasis on approximation algorithms for hard problems, metric embeddings and algorithmic techniques for big data. His work on dimension reduction won the best paper award at FOCS 2003. He was awarded the 2012 Paris Kanellakis Theory and Practice Award for his work on locality sensitive hashing, and was named a Simons Investigator in theoretical computer science in 2014.