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  1. en.wikipedia.org › wiki › Ran_RazRan Raz - Wikipedia

    Ran Raz (Hebrew: רָן רָז) is a computer scientist who works in the area of computational complexity theory. He was a professor in the faculty of mathematics and computer science at the Weizmann Institute. He is now a professor of computer science at Princeton University.

    • Communication Complexity and Circuit Lower Bounds (1992)
    • Erdős Prize
  2. Ran Raz. Professor of Computer Science. Website: https://www.cs.princeton.edu/people/profile/ranr. Email: ranr@cs.princeton.edu. Office: Computer Science, 421. Phone: 609-258-2086. Research interests: Complexity theory, with emphasis on proving lower bounds for computational models: Boolean and arithmetic circuit complexity, communication ...

  3. 304. 1998. Exponential separation of quantum and classical communication complexity. R Raz. Proceedings of the thirty-first annual ACM symposium on Theory of computing …. , 1999. 297. 1999. Exponential separations for one-way quantum communication complexity, with applications to cryptography.

  4. Ran Raz é um cientista da computação israelense, que trabalha principalmente com teoria da complexidade computacional. Raz obteve um doutorado em 1992 na Universidade Hebraica de Jerusalém , orientado por Avi Wigderson , com a tese Communication Complexity and Circuit Lower Bounds .

    • Israel
    • 26 de dezembro de 1966, Jerusalém
  5. April 11th, 2024 Marcel Dall’Agnol joins the department as teaching faculty, bringing expertise in theoretical computer science; April 10th, 2024 Grad alum Avi Wigderson wins Turing Award for groundbreaking insights in computer science

  6. Real Analysis in Computer Science, Fall 2013, Visiting Scientist. Ran Raz is a Professor of Theoretical Computer Science at the Weizmann Institute of Science. He received his BSc in Mathematics and Physics and PhD in Mathematics from the Hebrew University, and after a postdoc at Princeton University joined the Weizmann Institute.

  7. dblp.org › pid › 91dblp: Ran Raz

    Ran Raz: Fast Learning Requires Good Memory: A Time-Space Lower Bound for Parity Learning. Electron. Colloquium Comput. Complex. TR16 (2016)