Pierre-Yves Gousenbourger is a Teaching Assistant at the École polytechnique de Louvain (Louvain School of Engineering), part of the Catholic University of Louvain. He is affiliated with the ICTEAM research institute and supervises students in the Department of Electrical Engineering. His pedagogical approach includes the Apprentissage Par Projet (project-based learning) methodology. He holds a PhD (2020) and a Master's in Applied Mathematics from UCLouvain, both supervised by Pierre-Antoine Absil. His doctoral thesis, Interpolation and fitting on Riemannian manifolds , developed efficient algorithms for manifold-valued data fitting with applications in UAV control, medical imaging, and model reduction. His research focuses on interpolation, optimization, and imaging on Riemannian manifolds , emphasizing Bézier curves/surfaces for data approximation. Key areas include wind field estimation via covariance matrix fitting on SPSD manifolds, medical shape reconstruction (e.g., endometrial surfaces), and parametric model order reduction using manifold interpolation. His work integrates Riemannian geometry with computational efficiency, leveraging tools like exponential/logarithm maps. His publications consistently address data fitting on nonlinear manifolds , featuring Bézier-based methods, variational formulations, and applications spanning computer vision, medical imaging, and aerodynamics. Trends include denoising corrupted data, accelerating computational workflows, and generalizing Euclidean splines to manifolds. He collaborates with the RANSO and Dysco research groups and has worked with institutions including MIT, TU Chemnitz, and the University of Münster. He develops open-source numerical tools for manifold interpolation, distributed under the GNU GPL license.
