Vladimir Vapnik

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Vladimir N. Vapnik
Born (1936-12-06) December 6, 1936 (age 87)
Tashkent, Uzbek SSR
Alma materInstitute of Control Sciences, Russian Academy of Sciences
Uzbek State University
Known forVapnik–Chervonenkis theory
Vapnik–Chervonenkis dimension
Support-vector machine
Support-vector clustering algorithm
Statistical learning theory
Structural risk minimization
AwardsKolmogorov Medal (2018)
IEEE John von Neumann Medal (2017)
Kampé de Fériet Award (2014)
C&C Prize (2013)
Benjamin Franklin Medal (2012)
IEEE Frank Rosenblatt Award (2012)
IEEE Neural Networks Pioneer Award (2010)
Paris Kanellakis Award (2008)
Fellow of the U.S. National Academy of Engineering (2006)
Gabor Award, International Neural Network Society (2005)
Alexander Humboldt Research Award (2003)
Scientific career
FieldsMachine learning
InstitutionsFacebook Artificial Intelligence Research
Vencore Labs
NEC Laboratories America
Adaptive Systems Research Department, AT&T Bell Laboratories
Royal Holloway, University of London
Columbia University
Doctoral advisorAlexander Lerner

Vladimir Naumovich Vapnik (Russian: Владимир Наумович Вапник; born 6 December 1936) is a computer scientist, researcher, and academic. He is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning[1] and the co-inventor of the support-vector machine method and support-vector clustering algorithms.[2]

Early life and education[edit]

Vladimir Vapnik was born to a Jewish family[3] in the Soviet Union. He received his master's degree in mathematics from the Uzbek State University, Samarkand, Uzbek SSR in 1958 and Ph.D in statistics at the Institute of Control Sciences, Moscow in 1964. He worked at this institute from 1961 to 1990 and became Head of the Computer Science Research Department.[4]

Academic career[edit]

At the end of 1990, Vladimir Vapnik moved to the USA and joined the Adaptive Systems Research Department at AT&T Bell Labs in Holmdel, New Jersey. While at AT&T, Vapnik and his colleagues did work on the support-vector machine, which he also worked on much earlier before moving to the USA. They demonstrated its performance on a number of problems of interest to the machine learning community, including handwriting recognition. The group later became the Image Processing Research Department of AT&T Laboratories when AT&T spun off Lucent Technologies in 1996. In 2000, Vapnik and neural networks expert, Hava Siegelmann developed Support-Vector Clustering, which enabled the algorithm to categorize inputs without labels - becoming one of the most ubiquitous data clustering applications in use. Vapnik left AT&T in 2002 and joined NEC Laboratories in Princeton, New Jersey, where he worked in the Machine Learning group. He also holds a Professor of Computer Science and Statistics position at Royal Holloway, University of London since 1995, as well as a position as Professor of Computer Science at Columbia University, New York City since 2003.[5] As of February 1, 2021, he has an h-index of 86 and, overall, his publications have been cited 226597 times.[6] His book on "The Nature of Statistical Learning Theory" alone has been cited 91650 times.

On November 25, 2014, Vapnik joined Facebook AI Research,[7] where he is working alongside his longtime collaborators Jason Weston, Léon Bottou, Ronan Collobert, and Yann LeCun.[8] In 2016, he also joined Peraton Labs.

Honors and awards[edit]

Vladimir Vapnik was inducted into the U.S. National Academy of Engineering in 2006. He received the 2005 Gabor Award,[9] the 2008 Paris Kanellakis Award, the 2010 Neural Networks Pioneer Award,[10] the 2012 IEEE Frank Rosenblatt Award, the 2012 Benjamin Franklin Medal in Computer and Cognitive Science from the Franklin Institute,[4] the 2013 C&C Prize from the NEC C&C Foundation,[11] the 2014 Kampé de Fériet Award, the 2017 IEEE John von Neumann Medal.[12] In 2018, he received the Kolmogorov Medal[13] from University of London and delivered the Kolmogorov Lecture. In 2019, Vladimir Vapnik received BBVA Foundation Frontiers of Knowledge Award.[citation needed]

Selected publications[edit]

  • On the uniform convergence of relative frequencies of events to their probabilities, co-author A. Y. Chervonenkis, 1971
  • Necessary and sufficient conditions for the uniform convergence of means to their expectations, co-author A. Y. Chervonenkis, 1981
  • Estimation of Dependences Based on Empirical Data, 1982
  • The Nature of Statistical Learning Theory, 1995
  • Statistical Learning Theory (1998). Wiley-Interscience, ISBN 0-471-03003-1.
  • Estimation of Dependences Based on Empirical Data, Reprint 2006 (Springer), also contains a philosophical essay on Empirical Inference Science, 2006

See also[edit]


  1. ^ Vapnik, Vladimir N. (2000). The Nature of Statistical Learning Theory | Vladimir Vapnik | Springer. doi:10.1007/978-1-4757-3264-1. ISBN 978-1-4419-3160-3. S2CID 7138354.
  2. ^ Cortes, Corinna; Vapnik, Vladimir (1995-09-01). "Support-vector networks". Machine Learning. 20 (3): 273–297. CiteSeerX doi:10.1007/BF00994018. ISSN 0885-6125. S2CID 206787478.
  3. ^ Estimation of Dependences Based on Empirical Data, (Springer Science & Business Media, 28 Sep 2006), By V. Vapnik, page 424
  4. ^ a b "Benjamin Franklin Medal in Computer and Cognitive Science". Franklin Institute. 2012. Retrieved April 6, 2013.
  5. ^ Scholkopf, Bernhard (2013). "Preface". Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. Springer. ISBN 978-3-642-41136-6.
  6. ^ "Google Scholar Record of Vapnik".
  7. ^ "Facebook AI Research". FAIR. Retrieved 2016-09-20.; "see also" "Facebook Research, ("People" entry for "Vladimir Vapnik")". Retrieved 2017-09-06.
  8. ^ "Facebook's AI team hires Vladimir Vapnik, father of the popular support vector machine algorithm". VentureBeat. 2014. Retrieved November 28, 2014.
  9. ^ "INNS awards recipients". International Neural Network Society. 2005. Retrieved November 28, 2014.
  10. ^ IEEE Computational Intelligence Society.
  11. ^ "NEC C&C Foundation Awards 2013 C&C Prize". NEC. 2013. Retrieved December 3, 2013.
  12. ^ "IEEE JOHN VON NEUMANN MEDAL RECIPIENTS" (PDF). Institute of Electrical and Electronics Engineers (IEEE).
  13. ^ "Kolmogorov Lecture and Medal".

External links[edit]