Biography

I am a fellow in AI (research fellow) at Paris School of AI of Université Paris Sciences et Lettres (PSL) since October 2025. My research activities take place within the Laboratory LAMSADE (Laboratoire d’analyse et modélisation de systèmes pour l’aide à la décision) of Université Paris Dauphine-PSL.
From 2024 to 2025, I was a postdoctoral researcher at the Laboratory of Signals and Systems (L2S), CentraleSupélec, CNRS, Université Paris-Saclay since October 2024, and from 2021 to 2024, I was a postdoc researcher within the TAU Team at INRIA, LISN, Université Paris-Saclay. My research focuses on causal learning, matrix and tensor decomposition and related topics in optimization. I obtained my PhD in May 2021 at Université catholique de Louvain, Belgium, under the supervision of Pierre-Antoine Absil and Kyle A. Gallivan. During my PhD I worked on matrix and tensor completion, graph-based machine learning, and optimization on matrix manifolds.

Publications and Preprints

• Learning Measurement Models via Subspace Identification and Clustering
  Shuyu Dong, Arthur Tenenhaus, Laurent Le Brusquet, Mohammed Nabil El Korso.
  IEEE Statistical Signal Processing Workshop (SSP), 2025, pp. 211-215.

• DCILP: A distributed approach for large-scale causal structure learning
  Shuyu Dong, Michèle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, and Koji Maruhashi. arXiv:2406.10481, 2025. In the 39th Annual AAAI Conference on Artificial Intelligence (AAAI-25).

• Learning Large Causal Structures from Inverse Covariance Matrix via Matrix Decomposition
  Shuyu Dong, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi, and Michèle Sebag. arXiv:2211.14221, 2024.

• From graphs to DAGs: a low-complexity model and a scalable algorithm
  Shuyu Dong and Michèle Sebag. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2022
  arXiv:2204.04644.

• On the analysis of optimization with fixed-rank matrices: a quotient geometric view
  Shuyu Dong, Bin Gao, Wen Huang, and Kyle A. Gallivan. arXiv:2203.06765, 2024.

• New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition
  Shuyu Dong, Bin Gao, Yu Guan, and François Glineur. SIAM Journal on Matrix Analysis and Applications 43 (2) (2022), 840-866.
  PDF

• Alternating minimization algorithms for graph regularized tensor completion
  Yu Guan, Shuyu Dong, Bin Gao, P.-A. Absil, and François Glineur. arXiv:2008.12876, 2023.

• Riemannian gradient descent methods for graph-regularized matrix completion
  Shuyu Dong, P.-A. Absil, and Kyle A. Gallivan. Linear Algebra and its Applications 623 (2021), 193-235
  PDF

• Preconditioned conjugate gradient algorithms for graph regularized matrix completion
  Shuyu Dong, P.-A. Absil, and Kyle A. Gallivan. The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2019.

• Graph learning for regularized low-rank matrix completion
  Shuyu Dong, P.-A. Absil, and Kyle A. Gallivan. 23rd International Symposium on Mathematical Theory of Networks and Systems (MTNS), 2018.

• Learning sparse models of diffusive graph signals
  Shuyu Dong, Dorina Thanou, P.-A. Absil, and Pascal Frossard. 25th European Symposium on Artificial Neural Networks (ESANN), 2017.