Teacher-Researcher
AAC
Algorithms, Learning and Computation
The main research axes of the department concern computational models and their robustness (from high-performance computing to quantum computing, including neural networks and distributed algorithms), processing architectures (graphs, distributed, synchronous or asynchronous processing), and methods (e.g., continuous, combinatorial, stochastic optimization; statistical learning and information theory). By construction, these research axes are the subject of collaborations with other departments, in particular Data Science and Fluid Mechanics-Energetics. The analysis and design of models and processes rely heavily on mathematical approaches (discrete and continuous, including probability, statistics and combinatorics) and statistical physics (in particular on the phase transition phenomena of complex systems), in conjunction with the LMO and CMAP (Maths and Maths. Appli), IJCLab (Physics), L2S (Signal Processing), as well as the LIX and the future LMF (Formal Methods) teams
Application areas include scientific computing (e.g., linear algebra, tensor calculus, numerical optimization, dynamical systems, simulation of quantum algorithms, computational mathematics and physics, systems of differential equations), distributed computing (e.g., cloud, scheduling, virtual currency, ubiquitous computing, autonomous robots, microbiological circuits), and data analysis.
Coordination
News
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Algorithmes Apprentissage et Calcul, Data Sciences
Prix de la meilleure contribution scientifique en STIC du plateau de Saclay 2022 attribué à Herilalaina Rakotoarison
Latest publications
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Logiciel
Sylvain Chevallier. MDLA – Multivariate Dictionary Learning Algorithm. 2026, ⟨swh:1:dir:1ed1898dbf4feb86843700917be3f7c7ef29efe5;origin=https://hal.archives-ouvertes.fr/hal-05676449;visit=swh:1:snp:e36ffb7cbcc1c58ae7bb7ca90ec9cbf68265d80c;anchor=swh:1:rel:c40398f508f9151f853ebc16cea30169c3d8f43a;path=/⟩. ⟨hal-05676449⟩
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Pré-publication, Document de travail
Théophile Sanchez, Pierre Jobic, Cyril Regan, Paul Verdu, Guillaume Charpiat, et al.. Attentive-SPIDNA: Attention-based neural networks for population genetics. 2026. ⟨hal-05688902⟩
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Pré-publication, Document de travail
Dylan Sechet, Marc Evrard, Matthieu Kowalski. Adapting offline models to a streaming context for music source separation. 2026. ⟨hal-05767849⟩
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Article dans une revue
Bruna Lopes, Gabriel Schwartz, Sylvain Chevallier, Raphael Y de Camargo, Bruno Aristimunha. Learning aligned EEG representations with subject-specific encoders. Scientific Reports, 2026, ⟨10.1038/s41598-026-69220-z⟩. ⟨hal-05758650⟩
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Communication dans un congrès
Sébastien Velut, Stella Douka, Théo Rudkiewicz, Alex Davey, Stéphane Rivaud, et al.. Tackling brain signal inter-subject variability with adaptive neural architectures. IJCNN 2026 – 2026 International Joint Conference on Neural Network, IEEE, Jun 2026, Maastricht, Netherlands. ⟨hal-05752385⟩
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Article dans une revue
Léo Planche, Anna Ilina, María C Ávila-Arcos, Flora Jay, Emilia Huerta-Sanchez, et al.. An archaic reference-free method to jointly infer Neanderthal and Denisovan introgressed segments in modern human genomes. Molecular Biology and Evolution, 2025, ⟨10.1093/molbev/msag235⟩. ⟨hal-05747173⟩
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Article dans une revue
Marco Rosario Capodiferro, Léo Planche, Emily M Breslin, Linda Ongaro, María C Ávila-Arcos, et al.. Archaic ancestry inference in imputed ancient human genomes. Nature Communications, 2026, 17 (1), pp.9480. ⟨10.1038/s41467-026-76204-0⟩. ⟨hal-05747161⟩
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Communication dans un congrès
Dylan Sechet, Marc Evrard, Matthieu Kowalski. Simulation-Based Inference for Plate Reverb System Identification. DAFX 2026 – 29th International Conference on Digital Audio Effects, Sep 2026, Cambridge, United States. ⟨hal-05742718⟩
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Communication dans un congrès
Brahim Driss, Alex Davey, Riad Akrour. PB$^2$: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning. RLC 2026 – Third Reinforcement Learning Conference, Aug 2026, Montreal, Canada. ⟨hal-05732524⟩
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Logiciel
Alexandre Barachant, Quentin Barthélemy, Gabriel Wagner Vom Berg, Alexandre Gramfort, Sylvain Chevallier, et al.. pyRiemann. 2026, ⟨swh:1:dir:99d04d80bb390054f73700b971236958727e9c55;origin=https://github.com/pyRiemann/pyRiemann;visit=swh:1:snp:42707203ccd68219f2c48dd52388fdaf0c91a420;anchor=swh:1:rev:854804d34bc7b40b318323520be896d97d8aa859⟩. ⟨hal-05676137⟩
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Logiciel
Grégoire Cattan, Quentin Barthélemy, Anton Andreev, Sylvain Chevallier, Vedant Patare, et al.. pyRiemann-qiskit. 2026, ⟨swh:1:dir:8b5612aedfe0d134a3dab99d574106412915ae4d;origin=https://github.com/pyRiemann/pyRiemann-qiskit;visit=swh:1:snp:9dfc0df92cc1fa97758f76c4b7b2f7314973f773;anchor=swh:1:rev:14df49d9c2bfed5372f8f59b5b8fa7120dfd1b6f⟩. ⟨hal-05676343⟩
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Communication dans un congrès
Bruno Aristimunha, Dung Truong, Pierre Guetschel, Seyed Yahya Shirazi, Isabelle Guyon, et al.. EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding. NeurIPS 2025 – 39th Annual Conference on Neural Information Processing Systems, Dec 2025, San Diego, United States. ⟨hal-05714360⟩
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Article dans une revue
Alice Lacan, Romain André, Michèle Sebag, Blaise Hanczar. In silico generation of gene expression profiles using diffusion models. BMC Bioinformatics, 2026, 27 (1), pp.160. ⟨10.1186/s12859-026-06470-8⟩. ⟨hal-05714728⟩
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Pré-publication, Document de travail
Bruno Aristimunha, Ce Ju, Antoine Collas, Florent Bouchard, Ammar Mian, et al.. SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization. 2026. ⟨hal-05714371⟩
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Communication dans un congrès
Alex Davey, Alena Shilova, Brahim Driss, Riad Akrour. StaQ: a Finite Memory Approach to Discrete Action Policy Mirror Descent. RLC 2026 – Third Reinforcement Learning Conference, Aug 2026, Montreal, Canada. ⟨hal-05710283⟩
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Communication dans un congrès
Benedictus Kent Rachmat, Thomas Gerald, Zheng Zhang, Cyril Grouin. QA Analysis in Medical and Legal Domains: A Survey of Data Augmentation in Low-Resource Settings. ACL 2025 – 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), ACL, Jul 2025, Vienna, Austria. pp.1132-1144, ⟨10.18653/v1/2025.acl-srw.89⟩. ⟨hal-05683004⟩
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Pré-publication, Document de travail
Marco Crialesi-Esposito, Alienor Riviere, Sergio Chibbaro. Sub-Kolmogorov Intermittency and Multifractal Dissipation in Multiphase Turbulence. 2026. ⟨hal-05679015⟩
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Pré-publication, Document de travail
Flavio Tuteri, Sergio Chibbaro, Alexandros Alexakis. Intermittency in Shell Models of Turbulent Cascades: from Single-Branch to Multi-Branch. 2026. ⟨hal-05679006⟩
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Logiciel
Bruno Aristimunha, Igor Carrara, Pierre Guetschel, Sara Sedlar, Pedro Rodrigues, et al.. Mother of all BCI Benchmarks. 2026, ⟨swh:1:dir:6d89a182d4e69572a73784c6616ef1cee5493d77⟩. ⟨hal-05675918⟩
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Thèse
Bruno Aristimunha Pinto. Learning Representations of Electroencephalogram using Deep Learning. Machine Learning [cs.LG]. Université Paris-Saclay; Universidade Federal do ABC, 2026. English. ⟨NNT : 2026UPASG016⟩. ⟨tel-05668261⟩
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Thèse
Eva Boguslawski. Politiques d’apprentissage par renforcement pour l’exploitation de réseaux électriques avec actions continues dans des architectures centralisées et décentralisées. Intelligence artificielle [cs.AIArtificial Intelligence]. Université Paris-Saclay, 2026. Français. ⟨NNT : 2026UPASG028⟩. ⟨tel-05657134⟩
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Thèse
Cyriaque Rousselot. Deep Learning for Spatio-Temporal Pesticide Exposure Modeling : Extrapolation, Interpretability, and Constraints. Computer Science [cs]. Université Paris-Saclay, 2026. English. ⟨NNT : 2026UPASG023⟩. ⟨tel-05653218⟩
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Poster de conférence
Louis Hernandez, Matthieu Boussard, Alessandro Leite. Harnessing Open-Source Large Language Models for Causal Discovery. EuroCIM 2025 – European causal inference meeting, Apr 2025, Ghent, Belgium. ⟨hal-05605983⟩
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Communication dans un congrès
Hugo Thimonier, José Lucas de Melo Costa, Fabrice Popineau, Arpad Rimmel, Bich-Liên Doan. T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data. ICLR 2025 – International Conference on Learning Representations, Apr 2025, Sinagpore, Singapore. ⟨10.48550/arXiv.2410.05016⟩. ⟨hal-05624276⟩
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Article dans une revue
Isabelle Hoxha, Sylvain Chevallier, Arnaud Delorme, Michel-Ange Amorim. Modality-specific predictive templates in pre-stimulus EEG activity. Neuropsychologia, 2026, 229, pp.109486. ⟨10.1016/j.neuropsychologia.2026.109486⟩. ⟨hal-05636990⟩
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Article dans une revue, Article dans une revue
Jean-Baptiste Malagnoux, Matthieu Kowalski. Connecting the Dots Between CDL and NMF: A Shared Framework for Time-Frequency Decomposition. IEEE Transactions on Signal Processing, 2026, 74, pp.2448-2460. ⟨10.1109/TSP.2026.3698440⟩. ⟨hal-05635856⟩
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Communication dans un congrès
Lucas Heck dos Santos, João Guilherme Prado Barbon, Sylvain Chevallier, Denis G Fantinato. Non-Linear Activation Functions for Deep Riemannian Neural Networks. ESANN 2026 – 34th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2026, Bruges, Belgium. pp.535-540, ⟨10.14428/esann/2026.ES2026-312⟩. ⟨hal-05616889⟩
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Article dans une revue
Li-Qing Jiang, Amanda Fay, Jens Daniel Müller, Luke Gregor, Alizée Roobaert, et al.. Synthesis of data products for ocean carbonate chemistry. Earth System Science Data, 2026, 18 (2), pp.1405 – 1462. ⟨10.5194/essd-18-1405-2026⟩. ⟨hal-05607765⟩
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Pré-publication, Document de travail
Alper Kalle, Théo Rudkiewicz, Mohamed-Oumar Ouerfelli, Mohamed Tamaazousti. Distribution-aware tensor decomposition for compression of convolutional neural networks. 2026. ⟨cea-05569665⟩
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Poster de conférence
Arnaud Quelin, Flora Jay, Frederic Austerlitz. Inferring demographic history of diverging populations from human genomic data. SMBE, Jul 2023, Ferrara, Italy. ⟨hal-05570993⟩