Tuesday 29 April 2014

Rajeev Motwani

About Rajeev Motwani

Rajeev Motwani (March 26, 1962 – June 5, 2009) was a professor of Computer Science at Stanford University whose research focused on theoretical computer science. He was an early advisor and supporter of companies including Google and PayPal, and a special advisor to Sequoia Capital. He was a winner of the Gödel Prize in 2001.

Career

Motwani joined Stanford soon after U.C. Berkeley. He founded the Mining Data at Stanford project (MIDAS), an umbrella organization for several groups looking into new and innovative data management concepts. His research included data privacy, web search, robotics, and computational drug design.

Motwani was one of the co-authors (with Larry Page and Sergey Brin, and Terry Winograd) of an influential early paper on the PageRank algorithm. He also co-authored another seminal search paper What Can You Do With A Web In Your Pocket with those same authors. PageRank was the basis for search techniques of Google (founded by Page and Brin), and Motwani advised or taught many of Google's developers and researchers.

He was an author of two widely used theoretical computer science textbooks: Randomized Algorithms with Prabhakar Raghavan and Introduction to Automata Theory, Languages, and Computation with John Hopcroft and Jeffrey Ullman.

He was an avid angel investor and helped fund a number of startups to emerge from Stanford. He sat on boards including Google, Kaboodle, Mimosa Systems (acquired by Iron Mountain Incorporated), Adchemy, Baynote, Vuclip, NeoPath Networks (acquired by Cisco Systems in 2007), Tapulous and Stanford Student Enterprises. He was active in the Business Association of Stanford Entrepreneurial Students (BASES).

He was a winner of the Gödel Prize in 2001 for his work on the PCP theorem and its applications to hardness of approximation.

He served on the editorial boards of SIAM Journal on Computing, Journal of Computer and System Sciences, ACM Transactions on Knowledge Discovery from Data, and IEEE Transactions on Knowledge and Data Engineering.

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