Mathieu Even

Mathieu Even

Postdoc at Inria Montpellier

Inria Montpellier, PREMEDICAL Research Project team

Biography

Starting October 1st, 2025, I am a researcher (CR, Chargé de Recherche) at Inria Montpellier, in the Premedical team. I am working on topics related to causal inference and machine learning applied to medical data. I also work with the startup Theremia Health.

Before that, I was a PhD student from September 2021 to June 2024 under the supervision of Laurent Massoulié, working on the theory and algorithms of Machine Learning, and a postdoc in the Premedical team between October 2024 and September 2025. My PhD manuscript is available here, for which I was awarded an accessit to the Gilles-Kahn PhD prize by the Société d’Informatique de France, sponsored by the French Académie des Sciences.

Interests
  • Optimization
  • Statistics
  • Causal Inference
  • Medical Applications
Education

  • PhD in Applied Mathematics and Computer Science (2021-2024)

    Inria Paris and ENS Paris

  • Master Program in Probability and Statistics, 2020

    Université d'Orsay/Paris-Saclay

  • Mathematics and Physics studies (2017-2021)

    Ecole Normale Supérieure de Paris

Publications

Christian Lebeda, Mathieu Even, Aurélien Bellet, Julie Josse (2025). Model Agnostic Differentially Private Causal Inference.

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Mathieu Even, Julie Josse (2025). Rethinking the Win Ratio A Causal Framework for Hierarchical Outcome Analysis.

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Oğuz Kaan Yüksel, Mathieu Even, Nicolas Flammarion (2024). Long-Context Linear System Identification.

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Mathieu Even, Hadrien Hendrikx, Laurent Massoulié (2024). Asynchronous speedup in decentralized optimization. IEEE Transactions on Automatic Control.

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Mathieu Even, Anastasia Koloskova, Laurent Massoulié (2024). Asynchronous SGD on Graphs - a Unified Framework for Asynchronous Decentralized and Federated Optimization. AISTATS, 2024.

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Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion (2023). Aligning Embeddings and Geometric Random Graphs, Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem. Neural Information Processing Systems (NeurIPS), 2023.

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Mathieu Even, Scott Pesme, Suriya Gunasekar, Nicolas Flammarion (2023). Implicit Regularisation, Large Stepsizes and Edge of Stability for (S)GD over Diagonal Linear Networks. Neural Information Processing Systems (NeurIPS), 2023.

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Mathieu Even (2023). Stochastic Gradient Descent under Markov Chain Sampling Schemes. International conference on Machine Learning, 2023.

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Konstantin Mischenko, Francis Bach, Mathieu Even, Blake Woodworth (2022). Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. Neural Information Processing Systems (NeurIPS), 2022.

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Edwige Cyffers, Mathieu Even, Aurélien Bellet, Laurent Massoulié (2022). Muffliato Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging. Neural Information Processing Systems (NeurIPS), 2022.

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Mathieu Even, Laurent Massoulié, Kevin Scaman (2022). On Sample Optimality in Personalized Federated and Collaborative Learning. Neural Information Processing Systems (NeurIPS), 2022.

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Mathieu Even, Raphaël Berthier, Francis Bach, Nicolas Flamarrion, Pierre Gaillard, Hadrien Hendrikx, Laurent Massoulié, Adrien Taylor (2021). A Continuized View on Nesterov Acceleration for Stochastic Gradient Descent and Randomized Gossip. NeurIPS2021 Outstanding Paper Award.

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Mathieu Even, Hadrien Hendrikx, Laurent Massoulié (2021). Decentralized Optimization with Heterogeneous Delays: a Continuous-Time Approach.

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RA Dragomir, Mathieu Even, Hadrien Hendrikx (2021). Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction. ICML2021.

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Mathieu Even, Laurent Massoulié (2021). Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization. COLT2021.

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Conferences and Workshops

- Prairie Workshop, Paris, October 10, 2021.

- Workshop On Future Synergies for Stochastic and Learning Algorithms, CIRM, Luminy, Sept. 27-Oct. 1, 2021.

- Conference on Learning Theory (COLT), August 15-19, 2021. (talk,slides,poster)

- International Conference on Machine Learning (ICML), July 18-24, 2021. (spotlight presentation)

Teaching

- Mathematics of Deep-Learning, ENS Paris, Fall 2021. Master course, TDs with Kevin Scaman. Course Website

- Network Algorithms, ENS Paris, Fall 2021. Master course, TDs with Ana Busic.

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