For the development of the theory and practice of boosting and its applications to machine learning
ABOUT THIS AWARD
The Paris Kanellakis Theory and Practice Award honors specific theoretical accomplishments that have had a significant and demonstrable effect on the practice of computing. This award is accompanied by a prize of $10,000 and is endowed by contributions from the Kanellakis family, with additional financial support provided by ACM's Special Interest Groups on Algorithms and Computational Theory (SIGACT), Design Automaton (SIGDA), Management of Data (SIGMOD), and Programming Languages (SIGPLAN), the ACM SIG Projects Fund, and individual contributions.
Andrei Broder, Moses Charikar, Piotr Indyk named recipients of the 2012 Paris Kanellakis Theory and Practice Award
Broder, Charikar, and Indyk were recognized for their work on algorithms that allow for quickly finding similar entries in large databases, known as locality-sensitive hashing (LSH), These algorithms can drastically reduce the computational time needed for retrieving similar items, at the cost of a small probability of failing to find the absolute closest match. LSH has impacted fields as diverse as computer vision, databases, information retrieval, data mining, machine learning, and signal processing.
Andrei Broder introduced specific locality-sensitive min-hash functions, used to estimate the similarity of data sets and identify near-duplicate documents. He is a Google Distinguished Scientist.
Piotr Indyk, with the late Rajeev Motwani, extended LSH functions to a wider range of distance functions, and applied them to design efficient approximate nearest neighbor algorithms. Indyk is a professor at MIT's Computer Science and Artificial Intelligence Lab.
Moses Charikar introduced sim-hash functions for angular distances. He is a professor of Computer Science at Princeton University.