Estelle Massart
Professor of applied mathematics - UCLouvain
Euler Building
Avenue Georges Lemaître, 4 - bte L4.05.01
B - 1348 Louvain-la-Neuve
Belgium
estelle.massart@uclouvain.be
Short biography
I graduated as an engineer in applied mathematics at
UCLouvain, Belgium, in 2015, and received a PhD in mathematical engineering from UCLouvain in 2019,
under the supervision of Julien Hendrickx
and Pierre-Antoine Absil. My PhD thesis, entitled
Data fitting on positive semidefinite matrix manifolds, was awarded the
XXI Alston Householder prize.
From 2020 to 2022, I was a postdoctoral researcher with the Mathematical Institute,
at the University of Oxford, where I was funded by the
National Physical Laboratory. From January to August 2022, I was an FNRS scientific collaborator at UCLouvain,
in the mathematical engineering department, a subdivision of the
ICTEAM institute. From September 2022, I am an academic faculty member of UCLouvain,
working in the mathematical engineering department.
Research interests
- Machine learning/deep learning
- Optimization
- Riemannian geometry
- Applications of mathematics to sciences (special interest in biomedical applications)
PhD students
Teaching
- LINFO1113, Algorithmique Numérique, 2022-2023
- LEPL1507 Projet en mathématiques appliquées, 2022-2023
- LINMA2120 Applied mathematics seminar, 2022-2023
- LSINC1313 Algorithmique Numérique, 2022-2023
- LINMA2361 Nonlinear dynamical systems, 2022-2023
Co-supervised MSc theses:
- Classification of covariance matrices for EEG: how to handle the low-rank case? D. Gailly, UCLouvain, 2022
- Accelerated First Order Methods for Non-convex Optimisation , M. Yu, University of Oxford, 2021
- Active Subspace Methods for Global Optimization , X. Liang, University of Oxford, 2021
- Positive-semidefinite matrix processing for EEG decoding and/or community detection, P. Veldeman, UCLouvain, 2021
- Classification of biologic signals on the symmetric positive definite manifold C. Vaes, UCLouvain, 2020
- Stochastic gradient methods for matrix completion, G. M. Bengoechea, UCLouvain, 2018
Publications
Theses
- E. Massart,
Data fitting on positive-semidefinite matrix manifolds,
PhD dissertation, UCLouvain, 2019.
- E. Massart,
Means and consensus on manifolds ,
MSc dissertation, UCLouvain, 2015.
Journal papers
- C. Cartis, E. Massart, A. Otemissov,
Global optimization using random embeddings, Mathematical Programming, 2022.
[Preprint]
[Publisher's version]
- C. Cartis, E. Massart, A. Otemissov,
Bound-constrained global optimization of functions with low effective dimensionality using multiple random embeddings,
Mathematical Programming, 2022.
[Preprint]
[Publisher's version]
- A. Musolas, E. Massart, J. M. Hendrickx, P.-A. Absil, Y. Marzouk
Low-rank multi-parametric covariance estimation,
BIT Numerical Mathematics 62, 221–249, 2022.
[Preprint]
[Publisher's version]
- E. Massart, P.-A. Absil,
Quotient geometry with simple geodesics for the manifold of fixed-rank positive-semidefinite matrices,
SIAM Journal on Matrix Analysis and Applications 41(1), 171-198, 2020.
[Preprint]
[Publisher's version]
Code available in the Manopt toolbox ( symfixedrankYYfactory.m )
- P.-Y. Gousenbourger, E. Massart, P.-A. Absil,
Data fitting on manifolds with composite Bézier-like curves and blended cubic splines,
Journal of Mathematical Imaging and Vision, 61(5), 645-671, 2019.
[Preprint]
[Publisher's version]
- E. M. Massart, J. M. Hendrickx, P.-A. Absil,
Matrix geometric means based on shuffled inductive sequences,
Linear Algebra and its Applications, 252, 334-359, 2018.
[Preprint]
[Publisher's version]
[Code]
Conference papers
- E. Massart,
Improving weight clipping in Wasserstein GANs, ICPR 2022.
[Preprint]
- E. Massart,
Orthogonal regularizers in deep learning: how to handle rectangular matrices?, ICPR 2022.
[Preprint]
- E. Massart, V. Abrol,
Coordinate Descent on the Orthogonal Group for Recurrent Neural Network Training,
To appear in the Proceedings of the 36th AAAI conference on artificial intelligence, 2022.
[Preprint]
- C. Cartis, E. Massart, A. Otemissov,
Dimensionality reduction techniques for global optimization of functions with low effective dimensionality,
ICML workshop “Beyond first order methods in ML systems”, 2020.
[Paper]
[3 minutes presentation video]
- N.T. Son, P.-Y. Gousenbourger, E. Massart, and T. Stykel,
Balanced Truncation for Parametric Linear Systems Using Interpolation of Gramians: A Comparison of Algebraic and Geometric Approaches,
Model Reduction of Complex Dynamical Systems, P. Benner, T. Breiten, H. Faßbender, M. Hinze, T. Stykel and R. Zimmermann Eds,
Springer Nature Switzerland - Birkhäuser, 31-51, 2022.
[Preprint]
[Publisher's version]
- B. Szczapa, M. Daoudi, S. Berretti, A. Del Bimbo, P. Pala, E. Massart,
Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition,
ICCV Human Behavior Understanding workshop, 2019.
[Preprint]
- E. Massart, J. M. Hendrickx, P.-A. Absil,
Curvature of the manifold of fixed-rank positive-semidefinite matrices endowed with the Bures-Wasserstein metric,
Geometric Science of Information: Fourth International Conference (GSI), 2019.
[Preprint]
[Publisher's version]
- N. Thanh Son, P.-Y. Gousenbourger, E. Massart, P.-A. Absil,
Online balanced truncation for linear time-varying systems using continuously differentiable interpolation on Grassmann manifold,
Proceedings of the 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019.
[Preprint]
[Publisher's version]
- E. Massart, P.-Y. Gousenbourger, N. Thanh Son, T. Stykel, P.-A. Absil,
Interpolation on the manifold of fixed-rank positive-semidefinite matrices for parametric model order reduction: preliminary results,
Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 281-286, 2019.
[Publisher's version]
- E. Massart, S. Chevallier,
Inductive means and sequences applied to online classification of EEG,
Geometric Science of Information: Third International Conference (GSI), 2017.
[Preprint]
[Publisher's version]
[Talk]
- P.-Y. Gousenbourger, E. Massart, A. Musolas, P.-A. Absil, J. M. Hendrickx, L. Jacques, Y. Marzouk,
Piecewise-Bezier C1 smoothing on manifolds with application to wind field estimation,
Proceedings of the 25th European Symposium on Artifical Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 305-310, 2017.
[Publisher's version]
- E. M. Massart, J. M. Hendrickx, P.-A. Absil,
Extending a two-variable mean to a multi-variable mean,
24th European Symposium on Artifical Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2016.
[Publisher's version]
[Code]