Paris Kanellakis Award

The Paris Kanellakis Theory and Practice Award is granted yearly by the Association for Computing Machinery (ACM) to honor "specific theoretical accomplishments that have had a significant and demonstrable effect on the practice of computing". It was instituted in 1996, in memory of Paris C. Kanellakis, a computer scientist who died with his immediate family in an airplane crash in South America in 1995 (American Airlines Flight 965). The award is accompanied by a prize of $10,000 and is endowed by contributions from Kanellakis's parents, with additional financial support provided by four ACM Special Interest Groups (SIGACT, SIGDA, SIGMOD, and SIGPLAN), the ACM SIG Projects Fund, and individual contributions.

Winners of the Paris Kanellakis Theory and Practice Award
  • Adleman, Diffie, Hellman, Merkle, Rivest, Shamir
  • Lempel, Ziv
  • Bryant, Clarke, Emerson, McMillan
  • Sleator, Tarjan
  • Karmarkar
  • Myers
  • Franaszek
  • Miller, Rabin, Solovay, Strassen
  • Freund, Schapire
  • Holzmann, Kurshan, Vardi, Wolper
  • Brayton
  • Buchberger
  • Cortes, Vapnik
  • Bellare, Rogaway
  • Mehlhorn
  • Samet
  • Broder, Charikar, Indyk
  • 1996: The first award was split among six researchers, Leonard Adleman, Whitfield Diffie, Martin Hellman, Ralph Merkle, Ronald Rivest, and Adi Shamir, for "the conception and first effective realization of public-key cryptography".
  • 1997: The second award was given to Abraham Lempel and Jacob Ziv, for their pioneering work in data compression, leading to their LZ algorithm which "yields the best compression rate achievable by finite-state encoders" and "can be found in virtually every modern computer".
  • 1998: The award was shared by Randal Bryant, Edmund M. Clarke, E. Allen Emerson, and Kenneth L. McMillan, for "their invention of 'symbolic model checking,' a method of formally checking system designs widely used in the computer hardware industry".
  • 1999: The ACM bestowed the award to Daniel Sleator and Robert Tarjan, for "creating the Splay-Tree Data Structure one of the most widely-used data structures invented in the last 20 years".
  • 2000: The winner was Narendra Karmarkar, for "his theoretical work in devising an Interior Point method for linear programming that provably runs in polynomial time, and for his implementation work suggesting that Interior Point methods could be effective for linear programming in practice as well as theory".
  • 2001: The ACM honored Eugene Myers, for "his contribution to sequencing the human genome, the complete DNA content of a human cell, and encoding all of its genes, the basic building blocks of life".
  • 2002: The award went to Peter Franaszek, for "his seminal and sustained contributions to the theory and application of constrained channel coding".
  • 2003: The ACM honored Gary Miller, Michael Rabin, Robert Solovay, and Volker Strassen, for "their contributions to realizing the practical uses of cryptography and for demonstrating the power of algorithms that make random choices", through work which "led to two probabilistic primality tests, known as the Solovay–Strassen test and the Miller–Rabin test".
  • 2004: The winners were Yoav Freund and Robert Schapire, for their "seminal work and distinguished contributions to the development of the theory and practice of boosting, a general and provably effective method of producing arbitrarily accurate prediction rules by combining weak learning rules"; specifically, for AdaBoost, their machine learning algorithm which "can be used to significantly reduce the error of algorithms used in statistical analysis, spam filtering, fraud detection, optical character recognition, and market segmentation, among other applications".
  • 2005: The ACM honored Gerard Holzmann, Robert Kurshan, Moshe Y. Vardi, and Pierre Wolper, for "their contribution to techniques that provide powerful formal verification tools for hardware and software systems".
  • 2006: The winner was Robert Brayton, for " his innovative contributions to logic synthesis and electronic system simulation, which have made possible rapid circuit design technologies for the electronic design automation industry".
  • 2007: The award was given to Bruno Buchberger, for "his role in developing the theory of Groebner Bases, which has become a crucial building block to computer algebra, and is widely used in science, engineering, and computer science".
  • 2008: The award was shared by Corinna Cortes and Vladimir Vapnik, for "their revolutionary development of a highly effective algorithm known as Support Vector Machines (SVM), a set of related supervised learning methods used for data classification and regression", which is "one of the most frequently used algorithms in machine learning, and is used in medical diagnosis, weather forecasting, and intrusion detection among many other practical applications".
  • 2009: The winners were Mihir Bellare and Phillip Rogaway, for "their development of practice-oriented provable security, which has resulted in high-quality, cost-effective cryptography, a key component for Internet security in an era of explosive growth in online transactions".
  • 2010: The award went to Kurt Mehlhorn, for "contributions to algorithm engineering that led to creation of the Library of Efficient Data types and Algorithms (LEDA)", a software collection of data structures and algorithms which "has been incorporated in the applied research programs of thousands of companies worldwide in telecommunications, bioinformatics, Computer-aided design (CAD) and Geographic Information System (GIS), banking, optical products, and transportation".
  • 2011: The winner was professor Hanan Samet, for "pioneering research on quadtrees and other multidimensional spatial data structures for sorting spatial information, as well as his well-received books, which have profoundly influenced the theory and application of these structures".
  • 2012: The winners were Andrei Broder, Moses S Charikar and Piotr Indyk for "their groundbreaking work on Locality-Sensitive Hashing that has had great impact in many fields of computer science including computer vision, databases, information retrieval, machine learning, and signal processing".

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