Discrete Mathematics and Optimization Seminar

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Bernard Chazelle
Princeton University
Monday September 12th at 4.30pm
Burnside 1205



Title. Data-Powered Computing

Abstract. The traditional view of computing puts the spotlight on the output rather than on the input. The main reason for
this -- besides the obvious one, which is that the input is what we have but the output is what we want --
is that computational power is often gained by lowering our expectations about the output side of the computing
pipeline (think of randomization, approximation, etc).

A shift in focus toward the "input" has occurred lately, however, causing a revolution of sorts in algorithm design.
Computing with massive data sets, data streaming, coping with uncertainty, priced computation, property testing, and sublinear
algorithms are all parts of the story. I will discuss the past and present of this development and speculate about its future;
all in the (biased) context of my own research.