Streaming algorithms for frequency-based functions and k-mean clustering


Event Details
Thursday, April 3, 2014
Talk:
4:00 p.m., Avery 115

Reception:
3:30 p.m., Avery 348

Vladimir Breverman, Ph.D.

Assistant Professor, Johns Hopkins University

Abstract

The streaming model of computation is an important area of theoretical
computer science with many practical applications. In this talk we will define
the streaming model, explain some fundamental streaming methods, survey recent
results and discuss current challenges and open problems. In particular, we
will present new streaming algorithms for frequency-based functions, frequency
moments and k-mean clustering.

Speaker Bio

Vladimir Braverman is an Assistant Professor with the Department of Computer
Science at the Johns Hopkins University. His main research interests are
randomized and streaming algorithms. Vladimir obtained his B.Sc. and M.Sc.
degrees from Ben-Gurion University of the Negev, Israel, and his Ph.D. from
UCLA in 2011. Prior to attending UCLA, Braverman has led a research team at
HyperRoll, a startup company that has been acquired by Oracle in 2009.