Property Testing is the study of super-fast (randomized) algorithms for approximate decision making. These algorithms are given direct access to items of a huge data set, and determine whether this data set has some predetermined (global) property or is far from having this property. Remarkably, this approximate decision is made by accessing a small portion of the data set.
Property Testing has been a subject of intensive research in the last couple of decades, with hundreds of studies conducted in it and in closely related areas. Indeed, Property Testing is closely related to Probabilistically Checkable Proofs (PCPs), and is related to Coding Theory, Combinatorics, Statistics, Computational Learning Theory, Computational Geometry, and more.
The mini-workshop, hosted by the Institute for Computer Science (ITCS) at Tsinghua University (Beijing), brought together a couple of dozen of leading international researchers in Property Testing and related areas.
See the official brochure (in PDF) and the program (in HTML).
Oded Goldreich's homepage.