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Moses Charikar is the Donald E. Knuth Professor of Computer Science at Stanford University. He obtained his PhD from Stanford in 2000 and has held faculty positions, including a tenure at Princeton University from 2001 to 2015. Charikar's research focuses on developing efficient algorithmic techniques for processing and searching large high-dimensional data sets. His work spans various domains of computational problems, machine learning, approximation algorithms, and convex optimization. He has received multiple accolades for his contributions, including the Paris Kanellakis Theory and Practice Award in 2012 and being named a Simons Investigator in theoretical computer science in 2014. Charikar’s research interests and work have led him to be recognized as an ACM Fellow in 2021, as well as other prestigious awards throughout his career for his influential papers in the field. His work on locality sensitive hashing and algorithms for discrete optimization problems has proven essential in theoretical and practical applications across computer science.
A method for achieving lossless compression of fragmented image data.
Method and apparatus for lossless compression.
Method for identifying the format of fragmented data.
Further enhancements in lossless image data compression.
Improved methods for lossless compression of fragmented data.
Identification methods for formats of fragmented data.
Techniques for fragmented data identification.
A system for performing similarity searches using compact data structures.
A method for managing non-transferable anonymous credentials.
Techniques for estimating similarity in data.
The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.