@inproceedings{NavaYazdaniHegevonTycowicz2019, author = {Nava-Yazdani, Esfandiar and Hege, Hans-Christian and von Tycowicz, Christoph}, title = {A Geodesic Mixed Effects Model in Kendall's Shape Space}, volume = {11846}, booktitle = {Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA)}, doi = {10.1007/978-3-030-33226-6_22}, pages = {209 -- 218}, year = {2019}, abstract = {In many applications, geodesic hierarchical models are adequate for the study of temporal observations. We employ such a model derived for manifold-valued data to Kendall's shape space. In particular, instead of the Sasaki metric, we adapt a functional-based metric, which increases the computational efficiency and does not require the implementation of the curvature tensor. We propose the corresponding variational time discretization of geodesics and apply the approach for the estimation of group trends and statistical testing of 3D shapes derived from an open access longitudinal imaging study on osteoarthritis.}, language = {en} } @inproceedings{Navayazdani2025, author = {Navayazdani, Esfandiar}, title = {Ridge Regression for Manifold-valued Time-Series with Application to Meteorological Forecast}, booktitle = {Geometric Science of Information}, publisher = {Springer Nature}, arxiv = {http://arxiv.org/abs/2411.18339v2}, doi = {10.1007/978-3-032-03921-7_1}, pages = {3 -- 11}, year = {2025}, abstract = {We propose a natural intrinsic extension of the ridge regression from Euclidean spaces to general manifolds, which relies on Riemannian least-squares fitting, empirical covariance, and Mahalanobis distance. We utilize it for time-series prediction and apply the approach to forecast hurricane tracks and their wind speeds.}, language = {en} } @inproceedings{Navayazdani2024, author = {Navayazdani, Esfandiar}, title = {Elastic Analysis of Augmented Curves and Constrained Surfaces}, booktitle = {Proc. of IAPR Third International Conference on Discrete Geometry and Mathematical Morphology}, publisher = {Springer}, arxiv = {http://arxiv.org/abs/2402.04944}, doi = {10.1007/978-3-031-57793-2_27}, pages = {353 -- 363}, year = {2024}, language = {en} } @inproceedings{MyersUtpalaTalbaretal.2022, author = {Myers, Adele and Utpala, Saiteja and Talbar, Shubham and Sanborn, Sophia and Shewmake, Christian and Donnat, Claire and Mathe, Johan and Lupo, Umberto and Sonthalia, Rishi and Cui, Xinyue and Szwagier, Tom and Pignet, Arthur and Bergsson, Andri and Hauberg, S{\o}ren and Nielsen, Dmitriy and Sommer, Stefan and Klindt, David and Hermansen, Erik and Vaupel, Melvin and Dunn, Benjamin and Xiong, Jeffrey and Aharony, Noga and Pe'er, Itsik and Ambellan, Felix and Hanik, Martin and Navayazdani, Esfandiar and Tycowicz, Christoph von and Miolane, Nina}, title = {ICLR 2022 Challenge for Computational Geomerty \& Topology: Design and Results}, volume = {196}, booktitle = {Proceedings of Topology, Algebra, and Geometry in Learning}, publisher = {PMLR}, arxiv = {http://arxiv.org/abs/2206.09048}, pages = {269 -- 276}, year = {2022}, language = {en} }