Alexandre Hippert-Ferrer

Alexandre Hippert-Ferrer is an Associate Professor at Université Gustave Eiffel, where he is a permanent member of the LASTIG lab working in the Strudel team. He primarily teaches at the National School of Geographic Sciences (École Nationale des Sciences Géographiques).

Dr. Hippert-Ferrer received his PhD from University Savoie Mont Blanc, Annecy, France in 2020, supervised by Philippe Bolon and Yajing Yan. His doctoral research focused on reconstruction of missing data in displacement time series by remote sensing. Following his PhD, he was a post-doctoral researcher at L2S (Université Paris-Saclay) working with Florent Bouchard and Frédéric Pascal. He maintains active collaborations with researchers including Arnaud Breloy and Nabil El Korso from LEME at University of Paris Nanterre.

Hippert-Ferrer's research focuses on robust statistical methods for signal processing, particularly for geospatial data with missing values. His work bridges the fields of remote sensing, signal processing, and statistics, with a strong emphasis on practical applications in earth observation and geodesy. He has developed innovative approaches using Empirical Orthogonal Function (EOF) analysis, Expectation-Maximization (EM) algorithms, and Riemannian geometry for handling incomplete data in displacement measurement time series. His methods have been applied to Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) data for monitoring ground displacement.

His publication record shows a clear progression from basic gap-filling techniques to sophisticated statistical models. Starting with EOF-based approaches, his work has evolved to incorporate advanced methods including elliptical distributions, low-rank covariance estimation, and graphical factor models with Riemannian optimization. The consistent theme across his publications is developing mathematically sound yet practical algorithms for real-world geospatial data challenges, particularly when data is incomplete or corrupted.

Dr. Hippert-Ferrer has made significant contributions through publications in top journals including IEEE Transactions on Signal Processing and IEEE Transactions on Geoscience and Remote Sensing. His open-source implementation of the EM-EOF algorithm on GitHub demonstrates his commitment to reproducible research. As an educator at the National School of Geographic Sciences, he trains the next generation of geospatial professionals, bridging theoretical statistical methods with practical applications in earth observation.