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  • Miolane, N., Caorsi, M., Lupo, U., Guerard, M., Guigui, N., Mathe, J., Cabanes, Y., Reise, W., Davies, T., Leitão, A., Mohapatra, S., Utpala, S., Shailja, S., Corso, G., Liu, G., Iuricich, F., Manolache, A., Nistor, M., Bejan, M., Mihai Nicolicioiu, A., Luchian, B.-A., Stupariu, M.-S., Michel, F., Dao Duc, K., Abdulrahman, B., Beketov, M., Maignant, E., Liu, Z., Černý, M., Bauw, M., Velasco-Forero, S., Angulo, J., Long Y. ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results. Workshop on Geometrical and Topologic Representation Learning (ICLR) (2021).

 

 

  • Miolane, N., Guigui, N., Zaatiti, H., Shewmake, C., Hajri, H., Brooks, D., Le Brigant, A., Mathe, J. Hou, B., Thanwerdas, Y., Heyder, S., Peltre, O., Koep, N., Cabanes, Y., Gerald, T. Chauchat, P., Kainz, B., Donnat, C., Holmes, S., Pennec, X. Introduction to Geometric Learning in Python with Geomstats. Conference on Scientific Computing in Python (SciPy). (2020).

 

  • Miolane, N., Guigui, N., Le Brigant, A., Mathe, J., Hou, B., Thanwerdas, Y., Heyder, S., Peltre, O., Koep, N., Cabanes, Y., Chauchat, P., Zaatiti, H., Hajri, H., Gerald, T. , Shewmake, C., Brooks, D., Kainz, B., Donnat, C., Holmes, S., Pennec, X. Geomstats: A Python package for Riemannian geometry in Machine Learning. Journal of Machine Learning Research (JMLR) (2020).

 

 

 

 

 

 

 

  • Miolane, N., Poitevin, F., Holmes, S. Exploring Cryo-EM Latent Space with Variational Autoencoders. Bio-X workshop on Cryo-Electron Microscopy, Stanford, USA. (2019)

 

  • Poitevin, F., Li, Y.T., Miolane, N., Gati, C., Levitt, M. Convenience Tools to Explore Variability in Cryo-EM Data. Bio-X workshop on Cryo-Electron Microscopy, Stanford, USA, (2019).

 

 

 

 

 

  • Miolane, N., Pennec, X., Holmes, S. Toward a unified geometric Bayesian framework for template estimation in Computational Anatomy. World Meeting of the International Society for Bayesian Analysis (ISBA). 2016. (Young Researcher Travel Award).

 

 

 

 

 

 

  • Miolane, N., Khanal, B.: Statistics on Lie groups for Computational Anatomy. Video for the Educational Challenge of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MIT Boston. 2014. (Video, 1st Popular Prize).