Wikipedia

Gabriel Peyré

Gabriel Peyré (born 1979)[1] is a French mathematician. Most of his work lies in the field of transportation theory. He is a CNRS senior researcher and a Professor in the mathematics and applications department of the École normale supérieure in Paris.[2] He was awarded the CNRS Silver Medal in 2021.[3]

Gabriel Peyré
NationalityFrench
AwardsBlaise Pascal Prize (2017) of the Académie des sciences
Enrico Magenes Prize (2019) of the Unione Matematica Italiana
Scientific career
FieldsApplied mathematics
InstitutionsENS and CNRS

Life and work edit

His work mainly focuses on applied mathematics, in particular on the imaging sciences and machine learning applications of optimal transport.[4]

Gabriel Peyré is also the deputy director of the 3IA Paris Artificial Intelligence Research Institute[5] as well as a member of the scientific committee of the ENS center for data science.[6] He is also the creator of the Numerical tour of data science,[7] a popular online repository of Python/Matlab/Julia/R resources to teach mathematical data sciences. He is a frequent collaborator of the INRIA team Mokaplan.[8]

Awards and distinctions edit

Gabriel Peyré was awarded the Blaise Pascal Prize in 2017 from the Académie des sciences[9] as well as the Enrico Magenes Prize (2019) from the Unione Matematica Italiana.[10] He also was an invited speaker at the European Congress of Mathematics in 2020.[11] His research was supported by an ERC starting grant in 2012 and by an ERC consolidator grant in 2017.[12] In 2021, he was awarded the CNRS Silver Medal.[3]

Major publications edit

  • Benamou, J.-D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative bregman projections for regularized transportation problems [Publisher: Society for Industrial and Applied Mathematics]. SIAM Journalon Scientific Computing, 37(2), A1111–A1138.[13]
  • Peyré, G., Bougleux, S., & Cohen, L. (2008). Non-local regularization of inverse problems. In D. Forsyth, P. Torr, & A. Zisserman (Eds.), Computer vision – ECCV 2008 (pp. 57–68). Springer.[14]
  • Peyré, G., & Cuturi, M. (2019). Computational optimal transport: With applications to data science [Publisher: Now Publishers, Inc.]. Foundations and Trends in Machine Learning, 11(5), 355–607.[15]
  • Rabin, J., Peyré, G., Delon, J., & Bernot, M. (2012). Wasserstein barycenter and its application to texture mixing. In A. M. Bruckstein, B. M. ter Haar Romeny, A. M. Bronstein, & M. M. Bronstein (Eds.), Scale spaceand variational methods in computer vision (pp. 435–446). Springer.[16]
  • Solomon, J., de Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A., Du, T., & Guibas, L. (2015). Convolutional wasserstein distances: Efficient optimal transportation on geometric domains. ACM Transactions on Graphics, 34(4), 66:1–66:11.[17]

References edit

  1. ^ "Peyré, Gabriel (1979-....)". idref.fr. Retrieved 19 July 2021.
  2. ^ "Contact - Homepage of Gabriel Peyré". www.gpeyre.com. Retrieved 24 March 2021.
  3. ^ a b "Gabriel Peyré | CNRS". www.cnrs.fr (in French). Retrieved 17 January 2022.
  4. ^ "[Webinar] Gabriel Peyré ran a Seminar@SystemX on June 17, 2020 | IRT SystemX". Retrieved 4 March 2021.
  5. ^ "Governance | Prairie". 26 September 2019. Retrieved 24 March 2021.
  6. ^ "Data @ ENS - ENS-CFM Data Science Chair". data-ens.github.io. Retrieved 24 March 2021.
  7. ^ "Numerical Tours - A Numerical Tour of Data Science". www.numerical-tours.com. Retrieved 24 March 2021.
  8. ^ "Mokaplan". Inria. 21 July 2011. Retrieved 25 May 2021.
  9. ^ "Les prix de l'Académie des sciences 2017". www.academie-sciences.fr. Retrieved 24 March 2021.
  10. ^ "Premio "Enrico Magenes" – Sito dell'Unione Matematica Italiana" (in Italian). Retrieved 24 March 2021.
  11. ^ "8th European Congress of Mathematics". 8th European Congress of Mathematics. Retrieved 24 March 2021.
  12. ^ "NORIA - Homepage of Gabriel Peyré". www.gpeyre.com. Retrieved 24 March 2021.
  13. ^ Benamou, Jean-David; Carlier, Guillaume; Cuturi, Marco; Nenna, Luca; Peyré, Gabriel (2015). "Iterative Bregman Projections for Regularized Transportation Problems". SIAM Journal on Scientific Computing. 37 (2): A1111–A1138. arXiv:1412.5154. doi:10.1137/141000439. S2CID 12631372. Retrieved 9 April 2021.
  14. ^ Peyré, Gabriel; Bougleux, Sébastien; Cohen, Laurent (2008). "Non-local regularization of inverse problems". Computer Vision – ECCV 2008. Lecture Notes in Computer Science. Vol. 5304. pp. 57–68. doi:10.1007/978-3-540-88690-7_5. ISBN 978-3-540-88689-1. S2CID 1044368. Retrieved 9 April 2021.
  15. ^ "Computational optimal transport: With applications to data science". Retrieved 9 April 2021.
  16. ^ Rabin, Julien; Peyré, Gabriel; Delon, Julie; Bernot, Marc (2012). "Wasserstein barycenter and its application to texture mixing" (PDF). Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science. Vol. 6667. pp. 435–446. doi:10.1007/978-3-642-24785-9_37. ISBN 978-3-642-24784-2. S2CID 3571438.
  17. ^ "Convolutional wasserstein distances: Efficient optimal transportation on geometric domains". ACM Transactions on Graphics. doi:10.1145/2766963. S2CID 54500200.

External links edit

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