Biography:Isabelle Guyon

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Short description: French-born researcher in machine learning
Isabelle Guyon
Pronunciation
Born(1961-08-15)15 August 1961
Paris, France
CitizenshipFrench
Swiss
American
Alma materESPCI Paris (MSc)
Pierre and Marie Curie University (PhD)
Known forSupport Vector Machines
Siamese neural network
AwardsBBVA Foundation Frontiers of Knowledge Awards (2020)
AMIA Fellow (2011)
Scientific career
FieldsMachine Learning
InstitutionsBell Labs
University of Paris-Saclay
ThesisRéseaux de neurones pour la reconnaissance des formes : architectures et apprentissage (neural networks for pattern recognition) (1988)
Doctoral advisorGerard Dreyfus
Websitewww.clopinet.com/isabelle/

Isabelle Guyon (French pronunciation: ​[izabɛl ɡɥijɔ̃]; born August 15, 1961) is a French-born researcher in machine learning known for her work on support-vector machines, artificial neural networks and bioinformatics.[1] She is a Chair Professor at the University of Paris-Saclay.[2]

She is considered to be a pioneer in the field, with her contribution to the support-vector machines with Vladimir Vapnik and Bernhard Boser.[3][4]

Biography

After graduating from the French engineering school ESPCI Paris in 1985,[5] she joined the group of Gerard Dreyfus at the Université Pierre-et-Marie-Curie to do a PhD on neural networks architectures and training.[6][7]

Guyon defended her thesis in 1988 and was hired the year after at AT&T Bell Laboratories, first as a post-doc, then as a group leader.[4] She worked at Bell Labs for six years, where she explored several research areas, from neural networks to pattern recognition and computational learning theory, with application to handwriting recognition.[8] She collaborated with Yann LeCun, Léon Bottou, Vladimir Vapnik, Corinna Cortes, Yoshua Bengio, Patrice Simard, and met her future husband, Bernhard Boser.[1][4]

In 1996, Guyon left Bell Labs and raised her children at Berkeley, California.[1] In Berkeley, she created her own machine learning consulting company, Clopinet.[9] She became interested in medical applications, and used her previous work to classify the genes responsible for different types of cancers.[10]

Since 2003, Guyon has organized many challenges in data science, in order to stimulate research in this field.[4][11] She founded ChaLearn in 2011, a non-profit organization aimed at creating machine learning challenges open to everyone.[11] She was Program Chair of NeurIPS 2016[12] et became General Chair of NeurIPS in 2017.[13] She is also Action Editor for the Journal of Machine Learning Research[14] and Series Editor for Series: Challenges in Machine Learning.[15] She is a member of the European Laboratory for Learning and Intelligent Systems.[16]

In 2016, Guyon came back to France to take the Chair Professorship in Big data between the University of Paris-Saclay and INRIA.[3] She works in the group TAU (TAckling the Underspecified) of the Laboratoire de recherche en informatique.[17]

With Bernhard Schölkopf and Vladimir Vapnik, she received in 2020 the BBVA Foundation Frontiers of Knowledge Awards for her work in machine learning.[4]

Scientific work

Guyon has worked in many subfields of machine learning, including neural networks, support-vector machines, feature selection and applications of machine learning to biology.

Support-vector machines

Among her most notable contributions, Guyon co-invented support-vector machines (SVM) in 1992, with Bernhard Boser and Vladimir Vapnik.[18] SVM is a supervised machine learning algorithm, comparable to neural networks or decision trees, which has quickly become a classical technique in machine learning. SVMs have especially contributed to the popularization of kernel methods.

Neural networks

During her years at Bell Labs, Guyon took part of numerous projects involving neural networks. In particular, she wrote some of the first papers on the use of neural network for handwriting recognition using the MNIST database.[19] She is also a co-inventor of the siamese neural networks, a neural network architecture used to learn similarities, with applications to signature, face or object recognition.[10]

Machine learning for biology

Guyon is the author of many publications at the intersection of biology (cancer research and genomics) and artificial intelligence. She has notably introduced the use of support-vector machines to detect cancer using genes.[20]

Machine learning challenges

Through her non-profit organization Chalearn, Guyon has organized and directed challenges open to everyone in order to solve open problems in machine learning,[11] including computer vision,[21] neurosciences,[22] particle physics,[23] feature selection[24] and automated machine learning.[25] Most of the challenges organized by ChaLearn have resulted in publications. Among the most cited ones are:

  • Guyon et al., Result analysis of the NIPS 2003 feature selection challenge, Advances in neural information processing systems, 2005, link
  • Escalera et al., ChaLearn Looking at People Challenge 2014: Dataset and Results, Computer Vision - ECCV 2014 Workshops, Springer International Publishing, 2014, link
  • Guyon et al., A brief Review of the ChaLearn AutoML Challenge, JMLR: Workshop and Conference Proceedings 64:21-30, 2016, link
  • Adam-Bourdario et al., The Higgs boson machine learning challenge, JMLR: Workshop and Conference Proceedings 42:19-55, 2015, link

Private life

She is married to Bernhard Boser, a professor at UC Berkeley.[26] She has twins and one daughter, all three of whom have completed a science degree.[27] Guyon has three citizenships: French by birth, Swiss by marriage and American by naturalization.[1]

Awards and honors

Publications

  • Bernhard Boser, Isabelle Guyon and Vladmir Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, 1992, doi:10.1145/130385.130401
  • Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger and Roopak Shah, Signature verification using a" siamese" time delay neural network, Advances in Neural Information Processing Systems, 1994.
  • Isabelle Guyon and André Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, 2003.
  • Isabelle Guyon, Jason Weston, Stephen Barnhill and Vladimir Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning, Kluwer Academic Publishers, 2002, doi:10.1023/A:1012487302797

See also

References

  1. ↑ 1.0 1.1 1.2 1.3 Larousserie, David (2018-04-08). "Isabelle Guyon veut démocratiser l'intelligence artificielle" (in fr-FR). Le Monde. https://www.lemonde.fr/sciences/article/2018/04/08/isabelle-guyon-veut-democratiser-l-intelligence-artificielle_5282548_1650684.html. 
  2. ↑ "Des algorithmes qui apprennent et classent : le travail d'Isabelle Guyon récompensé" (in fr-FR). Université Paris-Saclay. 2020-05-28. https://www.universite-paris-saclay.fr/actualites/des-algorithmes-qui-apprennent-et-classent-le-travail-disabelle-guyon-recompense. 
  3. ↑ 3.0 3.1 "Pionnière : Isabelle Guyon, professeur à l'université de Paris-Saclay - Technos et Innovations" (in fr-FR). L'Usine nouvelle. 2018-02-07. https://www.usinenouvelle.com/editorial/pionniere-isabelle-guyon-professeur-a-l-universite-de-paris-saclay.N648608. 
  4. ↑ 4.0 4.1 4.2 4.3 4.4 4.5 "Isabelle Guyon". https://www.fbbva.es/en/galardonados/isabelle-guyon-2/. 
  5. ↑ ESPCI Alumnis. "Isabelle Boser (née Guyon), ingénieure de la 100ème promotion" (in fr-FR). https://www.espci.org/fr/anciens/eleves/100/Isabelle_Boser_(nee_Guyon). 
  6. ↑ Isabelle Guyon (1988) (in fr-FR). Réseaux de neurones pour la reconnaissance des formes : architectures et apprentissage. 
  7. ↑ "Home Page - Gérard Dreyfus" (in en). https://www.neurones.espci.fr/utilisateurs/Dreyfus/public_html/dreyfus-eng.htm. 
  8. ↑ Wang, Patrick S. P.; Guyon, Isabelle (1994-01-01). World Scientific. ed (in en). Advances In Pattern Recognition Systems Using Neural Network Technologies. World Scientific. ISBN 978-981-4611-81-7. https://books.google.com/books?id=LxK3CgAAQBAJ&q=Bell+Labs+Isabelle+Guyon&pg=PA43. Retrieved 2020-06-15. 
  9. ↑ Isabelle Guyon. "ClopiNet: Isabelle Guyon 's consulting company" (in en). http://www.clopinet.com/. 
  10. ↑ 10.0 10.1 Bromley, Jane; Guyon, Isabelle; LeCun, Yann; Säckinger, Eduard (1994). Morgan-Kaufmann. ed. "Signature Verification using a "Siamese" Time Delay Neural Network". Advances in Neural Information Processing Systems 6: 737–744. http://papers.nips.cc/paper/769-signature-verification-using-a-siamese-time-delay-neural-network.pdf. Retrieved 2020-06-15. 
  11. ↑ 11.0 11.1 11.2 "Chalearn: Challenges in Machine Learning" (in en). http://www.chalearn.org. 
  12. ↑ "NeurIPS 2016: Committees" (in en). https://neurips.cc/Conferences/2016/Committees. 
  13. ↑ "NeurIPS 2017: Committees" (in en). https://neurips.cc/Conferences/2017/Committees. 
  14. ↑ "Journal of Machine Learning Research: Editorial Board" (in en). http://www.jmlr.org/editorial-board.html. 
  15. ↑ "Series: Challenges in Machine Learning" (in en). http://www.mtome.com/Publications/CiML/ciml.html. 
  16. ↑ "Membres d'ELLIS" (in en). https://ellis.eu/members. 
  17. ↑ "TikiWiki | People". https://tao.lri.fr/tiki-index.php?page=People. 
  18. ↑ (in EN) A training algorithm for optimal margin classifiers | Proceedings of the fifth annual workshop on Computational learning theory. doi:10.1145/130385.130401. 
  19. ↑ Bottou, L.; Cortes, C.; Denker, J.S.; Drucker, H. (1994). "Comparison of classifier methods: A case study in handwritten digit recognition". Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5). 2. pp. 77–82 vol.2. doi:10.1109/ICPR.1994.576879. ISBN 0-8186-6270-0. 
  20. ↑ Guyon, Isabelle; Weston, Jason; Barnhill, Stephen; Vapnik, Vladimir (2002-01-01). "Gene Selection for Cancer Classification using Support Vector Machines" (in en). Machine Learning 46 (1): 389–422. doi:10.1023/A:1012487302797. ISSN 1573-0565. 
  21. ↑ "Looking at people: Chalearn workshop series" (in en). http://chalearnlap.cvc.uab.es/. 
  22. ↑ Springer International Publishing, ed (2017) (in en). Neural Connectomics Challenge. The Springer Series on Challenges in Machine Learning. ISBN 978-3-319-53069-7. https://www.springer.com/gp/book/9783319530697. Retrieved 2020-06-17. 
  23. ↑ "NIPS 2014 workshop: high-energy particle physics" (in en). 2014. https://sites.google.com/site/hepml14/home. 
  24. ↑ Springer-Verlag, ed (2006) (in en). Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing. ISBN 978-3-540-35487-1. https://www.springer.com/us/book/9783540354871. Retrieved 2020-06-17. 
  25. ↑ Springer International Publishing, ed (2019). "10" (in en). Automated Machine Learning: Methods, Systems, Challenges. The Springer Series on Challenges in Machine Learning. ISBN 978-3-030-05317-8. https://www.springer.com/de/book/9783030053178. Retrieved 2020-06-17. 
  26. ↑ "Bernhard Boser | EECS at UC Berkeley". https://www2.eecs.berkeley.edu/Faculty/Homepages/boser.html. 
  27. ↑ Anwar, Yasmin; May 11, Media Relations| (2020-05-11). "Rejection turned out great for Berkeley's top graduating senior" (in en-US). https://news.berkeley.edu/2020/05/11/rejection-turned-out-great-for-berkeleys-top-graduating-senior/. 
  28. ↑ "Isabelle Guyon, PhD, FACMI | AMIA". https://www.amia.org/about-amia/leadership/acmi-fellow/isabelle-guyon-phd-facmi. 

External links



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