Arora, Sanjeev

Autor: Arora, Sanjeev
Rok: 1968-

Biogr./Hist. údaje: Americký autor indického původu je profesorem počítačové vědy, zaměřený na teorii počítačové vědy.
Zdroj: Autoritní databáze Národní knihovny ČR

Sanjeev Arora (born January 1968) is an Indian American theoretical computer scientist who is best known for his work on probabilistically checkable proofs and, in particular, the PCP theorem. He is currently the Charles C. Fitzmorris Professor of Computer Science at Princeton University, and his research interests include computational complexity theory, uses of randomness in computation, probabilistically checkable proofs, computing approximate solutions to NP-hard problems, and geometric embeddings of metric spaces.He received a B.S. in Mathematics with Computer Science from MIT in 1990 and received a Ph.D. in Computer Science from the University of California, Berkeley in 1994 under Umesh Vazirani. Earlier, in 1986, Sanjeev Arora had topped the prestigious IIT JEE but transferred to MIT after 2 years at IIT Kanpur. He was a visiting scholar at the Institute for Advanced Study in 2002-03.His Ph.D. thesis on probabilistically checkable proofs received the ACM Doctoral Dissertation Award in 1995. He was awarded the Gödel Prize for his work on the PCP theorem in 2001 and again in 2010 for the discovery (concurrently with Joseph S. B. Mitchell) of a polynomial time approximation scheme for the euclidean travelling salesman problem. In 2008 he was inducted as a Fellow of the Association for Computing Machinery.In 2011 he was awarded the ACM Infosys Foundation Award, given to mid-career researchers in Computer Science. Arora has been awarded the Fulkerson Prize for 2012 for his work on improving the approximation ratio for graph separators and related problems (jointly with Satish Rao and Umesh Vazirani).He is a coauthor (with Boaz Barak) of the book Computational Complexity: A Modern Approach and is a founder, and on the Executive Board, of Princeton's Center for Computational Intractability. He and his coauthors have argued that certain financial products are associated with computational asymmetry which under certain conditions may lead to market instability.