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Moses Samson Charikar is an Indian computer scientist who works as a professor at Stanford University. He was previously a professor at Princeton University. The topics of his research include approximation algorithms, streaming algorithms, and metric embeddings. He is known for the creation of the SimHash algorithm used by Google for near duplicate detection. In 2012 he was awarded the Paris Kanellakis Award along with Andrei Broder and Piotr Indyk for their research on locality-sensitive hashing.

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  • Moses S. Charikar (* in Mumbai) ist ein indisch-US-amerikanischer theoretischer Informatiker. Er ist Professor an der Princeton University. Charikar besuchte das Indian Institute of Technology in Mumbai und wurde 2000 bei Rajeev Motwani an der Stanford University promoviert (Algorithms for Clustering Problems). Danach war er bei Google in einer Forschungsgruppe, bevor er 2001 an die Princeton University ging. Er befasst sich mit theoretischer Informatik speziell Näherungsalgorithmen für NP-schwere Probleme, metrische Einbettungen und Algorithmen für sehr umfangreiche Datenmengen. Er ist bekannt für die Einführung effizienter sogenannter Simhash-Funktionen bei Locality Sensitive Hashing (LSH). Auch an der Entwicklung von Minhash mit Andrei Broder war er beteiligt. 2012 erhielt er mit Broder und Piotr Indyk den Paris-Kanellakis-Preis für LSH. (de)
  • Moses Samson Charikar is an Indian computer scientist who works as a professor at Stanford University. He was previously a professor at Princeton University. The topics of his research include approximation algorithms, streaming algorithms, and metric embeddings. He is known for the creation of the SimHash algorithm used by Google for near duplicate detection. Charikar was born in Bombay, India, and competed for India at the 1990 and 1991 International Mathematical Olympiads, winning bronze and silver medals respectively. He did his undergraduate studies at the Indian Institute of Technology Bombay. In 2000 he completed a doctorate from Stanford University, under the supervision of Rajeev Motwani; he joined the Princeton faculty in 2001. In 2012 he was awarded the Paris Kanellakis Award along with Andrei Broder and Piotr Indyk for their research on locality-sensitive hashing. (en)
  • Moses Samson Charikar est un informaticien indien, professeur à l'université Stanford depuis 2015. Il était auparavant, de 2004 à 2015, professeur à l'université de Princeton. (fr)
  • Мойсей (Мозес) Самсон Чарикар - індійсько-американський науковець з інформатики та інформаційної безпеки, який працює професором у Стенфордському університеті (США). (uk)
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  • Moses Samson Charikar est un informaticien indien, professeur à l'université Stanford depuis 2015. Il était auparavant, de 2004 à 2015, professeur à l'université de Princeton. (fr)
  • Мойсей (Мозес) Самсон Чарикар - індійсько-американський науковець з інформатики та інформаційної безпеки, який працює професором у Стенфордському університеті (США). (uk)
  • Moses S. Charikar (* in Mumbai) ist ein indisch-US-amerikanischer theoretischer Informatiker. Er ist Professor an der Princeton University. Charikar besuchte das Indian Institute of Technology in Mumbai und wurde 2000 bei Rajeev Motwani an der Stanford University promoviert (Algorithms for Clustering Problems). Danach war er bei Google in einer Forschungsgruppe, bevor er 2001 an die Princeton University ging. 2012 erhielt er mit Broder und Piotr Indyk den Paris-Kanellakis-Preis für LSH. (de)
  • Moses Samson Charikar is an Indian computer scientist who works as a professor at Stanford University. He was previously a professor at Princeton University. The topics of his research include approximation algorithms, streaming algorithms, and metric embeddings. He is known for the creation of the SimHash algorithm used by Google for near duplicate detection. In 2012 he was awarded the Paris Kanellakis Award along with Andrei Broder and Piotr Indyk for their research on locality-sensitive hashing. (en)
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  • Moses S. Charikar (de)
  • Moses Charikar (fr)
  • Moses Charikar (en)
  • Мозес Чарикар (uk)
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