[PhD defense] Yann Thanwerdas
Yann Thanwerdas’ PhD defense took place on Tuesday, 24th of May, 2022 at 13:00, in the Morgenstern amphitheatre (Kahn building) at Inria Université Côte d’Azur and streamed live on Youtube.
[G-Stats Seminar] Erlend Grong
Erlend Grong visited our group on May 9, 2022 to give a talk on Most probable paths for anisotropic Brownian motion.
[G-Stats Seminar] Erik Bekkers
Erik Bekkers visited our group on April 28, 2022 to give a talk on Group equivariant deep learning and non-linear convolutions.
Geomstats Hackathon at Villa Clythia
Geomstats is an open-source Python package for Riemannian geometry in Machine Learning developed by an international team of researchers in which our team is strongly involved. We are happy to have organized our second coding sprint of the year at the Villa Clythia, in Frejus between April 4th – 8th….
Geomstats Hackathon at Inria
Geomstats is an open-source Python package for Riemannian geometry in Machine Learning developed by an international team of researchers in which our team is strongly involved. We are happy to have organized our first coding sprint of the year at the Inria Sophia Antipolis, between February 21- 25th. During this…
[G-Stats Seminar] Emmanuel Chevallier
Emmanuel Chevallier visited our group on February 24, 2022 to give a talk on Hyperbolic geometry and light polarization.
[G-Stats Seminar] Mathieu Carrière
Mathieu Carrière visited our group on February 10, 2022 to give a talk on A Framework to Differentiate Persistent Homology with Applications in Machine Learning and Statistics.
Inria DevTalks
Nicolas Guigui and Thomas Gerald presented geomstats at the 3rd Inria DevTalk on December 1st at the Sorbonne Center for Artificial Intelligence (SCAI), more info on the event page.
[G-Stats Seminar] Nicolas Duchateau
Nicolas Duchateau visited our group on November 19, 2021 to give a talk on Cardiac function analysis with representation learning.
[G-Stats Seminar] Stefan Horst Sommer
Stefan Horst Sommer visited our group on November 17, 2021 to give a talk on Stochastic shape analysis and probabilistic geometric statistics.
