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  1. Dive into the research topics where Bernard Chazelle is active. ... Dabke, D. V. & Chazelle, B., 2022, Complex Networks and Their Applications X - Volume 2, Proceedings of the 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021.

  2. Bernard Chazelle † Department of Computer Science Princeton University chazelle@cs.princeton.edu ABSTRACT We introduce a new low-distortion embedding of ‘d 2 into ‘O(log n) p (p = 1,2), called the Fast-Johnson-Lindenstrauss-Transform. The FJLT is faster than standard random pro-jections and just as easy to implement. It is based upon

  3. Bernard Chazelle (born November 5, 1955) is a French-American computer scientist. He is currently the Eugene Higgins Professor of Computer Science at Princeton University . Much of his work is in computational geometry , where he is known for his study of algorithms, such as linear-time triangulation of a simple polygon , as well as major complexity results, such as lower bound techniques ...

  4. 202 BERNARD CHAZELLE pointer-based data structure (Section 2). In two dimensions, the desired lower bound can be established by constructive means (Section 3). Things become a little more complicated in higher dimensions, so we turn to probabilistic arguments to prove the existence of “hard” inputs (Section 4).

  5. Bernard Chazelle Past Member. Bernard Chazelle. Addie and Harold Broitman Member in Biology. Affiliation Natural Sciences Field of Study Biology. Website Individual Website. From 9/2014 – 8/2015. Is there any benefit to be gained from ...

  6. simons.berkeley.edu › people › bernard-chazelleBernard Chazelle

    Bernard Chazelle. Professor, Princeton University His fundamental work is on geometry, randomness, and algorithms as they arise in nature. Website Opens new tab. The Simons Institute for the Theory of Computing is the world's leading venue for collaborative research in theoretical computer science. Footer. Programs & Events; About ...

  7. 1 de oct. de 2002 · Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer graphics, computer vision, molecular biology, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and ...