Computing Atlas

How Computing Was Built
Sign In
Text size
100%
Theme
Pioneer

Moses Charikar

Theory of Computation

Moses Samson Charikar is an Indian-American computer scientist who is a professor at Stanford University and was earlier a professor at Princeton University. His research covers approximation algorithms, streaming algorithms and metric embeddings, and he is known for creating the SimHash algorithm that Google uses to detect near duplicate documents. Born in Bombay, he competed for India at the 1990 and 1991 International Mathematical Olympiads, winning bronze and silver medals, then studied at the Indian Institute of Technology Bombay and completed a doctorate at Stanford in 2000 under Rajeev Motwani. In 2012 he shared the Paris Kanellakis Award with Andrei Broder and Piotr Indyk for work on locality-sensitive hashing.

Facts
Nationality
American 1
Nationality
Indian 1
Connections

In Field

Source Moses Charikar (Wikipedia)
In the Other Atlases

Nationality

Also in Geography Atlas: the nationality of this subject.

Source Moses Charikar (Wikipedia)Lead paragraph [nationality]
Quote, Lead paragraph [nationality]
Moses Samson Charikar is an Indian-American computer scientist

Also in Geography Atlas: the nationality of this subject.

Source Moses Charikar (Wikipedia)Lead paragraph [nationality]
Quote, Lead paragraph [nationality]
Moses Samson Charikar is an Indian-American computer scientist
Sources
1. Moses Charikar (Wikipedia)
  • Lead paragraph [nationality]
    Moses Samson Charikar is an Indian-American computer scientist
  • In Group: Algorithm Design and Analysis, Lead paragraph
    The topics of his research include approximation algorithms, streaming algorithms, and metric embeddings.
  • In Field: Algorithms and Complexity Theory, Lead paragraph
    The topics of his research include approximation algorithms, streaming algorithms, and metric embeddings.
View the Source
Comments (0)
No comments yet. Be the first to share a thought.
Reader Challenges (0)
No disputes yet. Spotted an error or a better source? Open the first one.