Biography:Gabriel Peyré

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Short description: French applied mathematician
Gabriel Peyré
NationalityFrance
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

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]

Life and work

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

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

  • 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

  1. ↑ "Peyré, Gabriel (1979-....)". https://www.idref.fr/077078128. 
  2. ↑ "Contact - Homepage of Gabriel Peyré". http://www.gpeyre.com/contact/. 
  3. ↑ 3.0 3.1 "Gabriel Peyré | CNRS" (in fr). https://www.cnrs.fr/fr/personne/gabriel-peyre. 
  4. ↑ "[Webinar Gabriel Peyré ran a Seminar@SystemX on June 17, 2020 | IRT SystemX"] (in en-US). https://www.irt-systemx.fr/en/evenements/webinar-gabriel-peyre-will-run-a-seminarsystemx-on-june-17-2020/. 
  5. ↑ "Governance | Prairie" (in en-GB). 2019-09-26. https://prairie-institute.fr/governance/. 
  6. ↑ "Data @ ENS - ENS-CFM Data Science Chair". https://data-ens.github.io/. 
  7. ↑ "Numerical Tours - A Numerical Tour of Data Science". https://www.numerical-tours.com/. 
  8. ↑ "Mokaplan". 21 July 2011. https://team.inria.fr/mokaplan/. 
  9. ↑ "Les prix de l'Académie des sciences 2017". https://www.academie-sciences.fr/pdf/documentation/prix2017/plaquette_prix_2017/index.html#89. 
  10. ↑ "Premio "Enrico Magenes" – Sito dell'Unione Matematica Italiana" (in it-IT). https://umi.dm.unibo.it/premi-old/premio-enrico-magenes/. 
  11. ↑ "8th European Congress of Mathematics". https://www.8ecm.si/program/plenary-speakers/36. 
  12. ↑ "NORIA - Homepage of Gabriel Peyré". http://www.gpeyre.com/noria/. 
  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. doi:10.1137/141000439. https://epubs.siam.org/doi/abs/10.1137/141000439. Retrieved 2021-04-09. 
  14. ↑ Peyré, Gabriel; Bougleux, Sébastien; Cohen, Laurent (2008). "Non-local regularization of inverse problems". Computer Vision – ECCV 2008. Lecture Notes in Computer Science. 5304. pp. 57–68. doi:10.1007/978-3-540-88690-7_5. ISBN 978-3-540-88689-1. https://link.springer.com/chapter/10.1007/978-3-540-88690-7_5. Retrieved 2021-04-09. 
  15. ↑ "Computational optimal transport: With applications to data science". https://www.nowpublishers.com/article/Details/MAL-073. 
  16. ↑ Rabin, Julien; Peyré, Gabriel; Delon, Julie; Bernot, Marc (2012). "Wasserstein barycenter and its application to texture mixing". Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science. 6667. pp. 435–446. doi:10.1007/978-3-642-24785-9_37. ISBN 978-3-642-24784-2. https://hal.archives-ouvertes.fr/hal-00476064/file/TexturesECCV10.pdf. 
  17. ↑ Convolutional wasserstein distances: Efficient optimal transportation on geometric domains. doi:10.1145/2766963. https://dl.acm.org/doi/abs/10.1145/2766963. 

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