Gabriel Peyré
French applied mathematician From Wikipedia, the free encyclopedia
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 | |
|---|---|
| Nationality | French |
| Awards | Blaise Pascal Prize (2017) of the Académie des sciences Enrico Magenes Prize (2019) of the Unione Matematica Italiana |
| Scientific career | |
| Fields | Applied mathematics |
| Institutions | ENS and CNRS |
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]
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]
- 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]
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