Estelle Massart

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

• Bastien Massion (2022-...), INMA-ICTEAM, UCLouvain.
• Timothé Taminiau (2023-...), INMA-ICTEAM, UCLouvain (joint supervision with G. Grapiglia, UCLouvain).
• David Thorsteinsson (2024-...), NUMA, KU Leuven (joint supervision with N. Vannieuwenhoven, KU Leuven).
• Jana Jovcheva (2024-...), INMA-ICTEAM, UCLouvain (joint supervision with N. Vannieuwenhoven, KU Leuven).
• Roy Makhlouf (2025-...), INMA-ICTEAM, UCLouvain.
• Antonin Oswald (2025-...), INMA-ICTEAM, UCLouvain (joint supervision with G. Bruno, UCLouvain).
• Martin Gerini (2026-...), INMA-ICTEAM, UCLouvain (joint supervision with J. Janquart, UCLouvain).

Teaching

• LINMA2474 High-Dimensional Data Analysis and Optimization
• LEPL1101 Algèbre
• LINFO1113 Algorithmique Numérique
• LEPL1507 Projet en mathématiques appliquées
• LINMA2120 Applied mathematics seminar
• LSINC1313 Algorithmique Numérique
• LINMA2361 Nonlinear dynamical systems

Co-supervised MSc theses:

• Modelling Motor Learning in the Elderly using a Serious Virtual Reality Game, L. Lernoux, 2026 (with G. Everard, Institute of Clinical and Experimental Research, UCLouvain)
• Benchmarking Machine Learning Architectures for Gene Regulatory Network Inference, E. Van Gysel, 2026
• Dynamic Evolution of EEG Synchronization for Epileptic Seizure Prediction, A. Nicolas, 2025
• Dimensionality Reduction for Global Optimization : Beyond Low Effective Dimension, A. Oswald, 2025
• Low-rank matrix factorization for compressing Transformers, N. Monhart, 2025
• Random embeddings for global optimization: Convergence results beyond isotropy, R. Makhlouf, 2025
• Investigation on EWS detection and classification through Deep Neural Networks, A. Guidat, 2025
• Early detection of Alzheimer disease from path integration data using machine learning and statistical analyses, A. Denis, 2025 (with L. Colmant, Institute of Neurosciences, UCLouvain)
• Numerical investigation of a continuous relaxation for the Column Subset Selection Problem, A. Victor, 2024
• On the training of shallow neural networks with the gradient method, Q. Brisbois, 2024 (with G. Grapiglia, UCLouvain)
• Interpretability of Deep Unsupervised Domain Adaptation, G. Dony, 2024 (with L. Jacques, UCLouvain)
• River Bed Profile Evolution Subject to Stochastic Forcing by Migrating Tributaries, C. Chevillard, 2024 (with H. Capart, NTU Taiwan)
• Numerical comparison of MCMC methods for Quantum Tomography, D. Mokeev, 2024 (with A. Thompson, NPL, UK)
• Classification of covariance matrices for EEG: how to handle the low-rank case? D. Gailly, 2022 (with P.-A. Absil)
• Accelerated First Order Methods for Non-convex Optimisation , M. Yu (University of Oxford), 2021 (with C. Cartis)
• Active Subspace Methods for Global Optimization , X. Liang (University of Oxford), 2021 (with C. Cartis)
• Positive-semidefinite matrix processing for EEG decoding and/or community detection, P. Veldeman, 2021 (with P.-A. Absil)
• Classification of biologic signals on the symmetric positive definite manifold C. Vaes, 2020 (with P.-A. Absil)
• Stochastic gradient methods for matrix completion, G. M. Bengoechea, 2018 (with P.-A. Absil, UCLouvain)

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.

Preprints

• E. Tansley, R. Makhlouf, E. Massart, C. Cartis, Efficient Techniques for Data Reconstruction, with Finite-Width Recovery Guarantees , under review, 2026. [Preprint]
• T. Taminiau, E. Massart, G. N. Grapiglia Riemannian optimization with finite-difference gradient approximations, under review, 2026. [Preprint]
• T. Adel, A. Agarwal, S. Chrétien, E. Massart, D. Mokeev, I. Rungger, A. Thompson, A Langevin sampler for quantum tomography, under review, 2025. [Preprint]

Journal papers

• C. Cartis, X. Liang, E. Massart, A. Otemissov, Learning the subspace of variation for global optimization of functions with low effective dimension, SIAM Journal on Optimization, 2026. [Preprint]
• T. Taminiau, G. N. Grapiglia, E. Massart Enhancing finite-difference based derivative-free optimization methods with machine learning, Optimization Letters, 2026. [Preprint] [Publisher's version]
• 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]

Book chapters

• 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]

Conference papers

• B. Massion, R. Makhlouf, E. Massart, The role of class encoding in neural collapse, accepted at EUSIPCO 2026 [Preprint].
• A. Oswald, E. Massart, Expressivity of congruence-based architectures for DNNs on positive-definite matrices, accepted at EUSIPCO 2026 [Preprint].
• E. Tansley, E. Massart, C. Cartis, On the Neural Feature Ansatz for Deep Neural Networks, AISTATS 2026. [Preprint]
• B. Massion, E. Massart, Grassmannian Frame Computation via Accelerated Alternating Projections , SAMPTA 2025. [Publisher version]
• E. Massart, V. Abrol, Coordinate descent on the Stiefel manifold for deep neural network training, ESANN 2023. [Publisher version]
• 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, AAAI 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]
• 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, 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, 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, ESANN 2019. [Publisher's version]
• E. Massart, S. Chevallier, Inductive means and sequences applied to online classification of EEG, 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, ESANN 2017. [Publisher's version]
• E. M. Massart, J. M. Hendrickx, P.-A. Absil, Extending a two-variable mean to a multi-variable mean, ESANN 2016. [Publisher's version] [Code]