Prasad Raghavendra
Alma materUniversity of Washington
Known forRaghavendra's theorem[1]
Awards
Scientific career
FieldsComputer science
InstitutionsUniversity of California at Berkeley
ThesisApproximating NP-hard Problems Efficient Algorithms and their Limits (2001)
Doctoral advisorVenkatesan Guruswami
Websitepeople.eecs.berkeley.edu/~prasad/

Prasad Raghavendra is an Indian-American theoretical computer scientist and mathematician, working in optimization, complexity theory, approximation algorithms, hardness of approximation and statistics. He is a professor of computer science at the University of California at Berkeley.[6]

Education

After completing a BSc at IIT Madras in 2005, he obtained an MSc (2007) and PhD (2009) at the University of Washington under the supervision of Venkatesan Guruswami. After a postdoctoral position at Microsoft Research New England, he became faculty at the University of California at Berkeley.

Career

Raghavendra showed that assuming the unique games conjecture, semidefinite programming is the optimal algorithm for solving constraint satisfaction problems.

Together with David Steurer, he developed the small set expansion hypothesis, for which they won the Michael and Shiela Held Prize in 2018.

He developed sum of squares as a versatile algorithmic technique. Together with David Steurer, he gave an invited talk on the topic at the 2018 ICM.

References

  1. ↑ Raghavendra, Prasad (17 May 2008). "Optimal Algorithms and Inapproximability Results for Every CSP?". STOC '08: Proceedings of the fortieth annual ACM symposium on Theory of computing. STOC '08. Victoria, BC: ACM. pp. 245–254. doi:10.1145/1374376.1374414.
  2. ↑ "News from the National Academy of Sciences". National Academy of Sciences. January 16, 2018.
  3. ↑ "The Research Grant Recipients". The Okawa Foundation. Retrieved December 1, 2023.
  4. ↑ "NSF Awards". Berkeley EECS. Retrieved December 1, 2023.
  5. ↑ "Fellows Databse". Alfred P. Sloan Foundation. Retrieved December 1, 2023.
  6. ↑ "CS Faculty List". Berkeley EECS. 23 November 2023.
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