I work on Machine Learning, at the intersection between Computer Science and Applied Mathematics.

Short bio: ...

• Publications – Google Scholar
• Long CV (last update: 2025)

Currently

Position: Permanent senior researcher at the Centre Giovanni Borelli¹, Ecole Normale Supérieure Paris-Saclay (ENS Paris-Saclay), University Paris-Saclay, France, and member of the Learning and Information Processing Systems (LIPS) research group². ...

Research interests & focus: Broad interest in statistical ML. Coordinator of the ML on Graphs research theme.

Research initiatives: Member of the Industrial Data Analytics and Machine Learning Chair, having as industrial partners SNCF, CEA, Michelin. Other Academia-Industry synergies: join project with RTE&SNCF, and collaboration with EDF. — Other initiatives: Since 2017, (co-)organizer of the LIPS seminars.

Past funding: ...

Current PhD students:

• Gaëtan Serré (~ global optimization)
• Gwendal Debaussart (~ graph representation learning)
• Eloi Campagne (~ time-series forecasting for the energy sector)
• Gaspard Abel (~ machine learning on social interaction networks)
• Gabriel Singer (~ statistical machine learning in uncertainty (imperfect labels))
• Jean Velluet (~ social network analysis on natural resources exploitation (fishery))

Alumni: ...

Announcements

News [bits and pieces]

• FMJH Post-doc funding: The Foundation has announced the call for post-doc funding 2027. Check a subject proposal entitled Predictive maintenance for networked systems. Applications are due to the 6th of December [link].
• ICTAI2026: the paper GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting, co-authored with E. Campagne, Y. Amara-Ouali, Y. Goude has been accepted to ICTAI 2026 [pdf][code].
• Preprints:
  • A Finslerian Approach for Embedding Directed Data, co-authored with G. Debaussart-Joniec and T. Blanchard, 2026 [pdf].
  • On the Calibration of Crowdsourcing Aggregators, co-authored with G. Singer, M. Frikha, R. Arab, S. Gruffaz, O. Vo Van, and N. Vayatis, 2026 [pdf].
  • polyview: A Python package for multi-view machine learning, co-authored with G. Debaussart-Joniec, 2026 [pdf][code].
  • Algorithmic collusion under asynchronous price updating, co-authored with I. Conjeaud and G. Abel, 2026 [pdf].
  • A model for imbalanced label aggregation: A focus on minority-class detection, co-authored with G. Singer, S. Gruffaz, O. Vo Van, and N. Vayatis, 2026 [pdf].
• New interns and a predoc:
  • Maïssa Frikha (M1 ENPC Paris, Aug-Feb 2026) on approaches fairness.
  • Alexandre Autran (M1 ENS Lyon, Sep-Feb 2026).
  • Hugo Gaible (Predoc, Sep-Dec 2026) on change-point detection.
• SETN2026: the paper GLOBe: A Modular Global Optimization library, co-authored with G. Serré and N. Vayatis has been accepted to SETN 2026 conference. It releases a library providing a collection of global optimization algorithms implemented in C++ and linked with Python [pdf][code].
• TMLR: the paper Generalized Dirichlet Energy and Graph Laplacians for Clustering Directed and Undirected Graphs has been accepted to the Transactions of Machine Learning Research. The work is co-authored with H. Sevi, G. Debaussart-Joniec, M. Jonckheere, and M. Hacini [pdf].
• ICML2026: The following papers have been accepted
  • Parametrized Power-Iteration Clustering for Directed Graphs, co-authored with G. Debaussart-Joniec, H. Sevi, and M. Jonckheere, 2026 [pdf][code][poster].
  • Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints, co-authored with G. Singer, S. Gruffaz, O. Vo Van, and N. Vayatis, 2026 [pdf][poster].
• New interns:
  • Ahmed El Fanaoui (M1 ENSTA Paris, Jun-Aug 2026) on approaches for graph profiling.
  • Nicolas Bigeard (M1 ESILV Paris, Jun-Jul 2026).
  • Matine ALIN (L3 SciencePo Paris, Jun-Jul 2026) on socioeconomic impact of certain social dilemmas.
  • Ahmed Benamar and Bernard Emilien (L3 ENS P.S. Maths, Mar-Jun 2026) on fairnsess.
• Invited Talk: Talk about the paper Multi-view diffusion geometry using intertwined diffusion trajectories, in the Geometry, Topology and Machine Learning Conference 2026, 18-22 May, Paris. [slides].
• ΤCSS: The article Uncovering Social Network Activity Using Joint User and Topic Interaction, co-authored with G. Abel, J.-P. Nadal, and J. randon-Furling, has been accepted to the journal IEEE Transactions on Computational Social Systems [pdf][code].
• Preprints:
  • Dual-Criterion Curriculum Learning:
    Application to Temporal Data, co-authored with G. Abel, E. Campagne, and M. Benloughmari, 2026. [pdf][code soon]
  • Enhancing LIME using Neural Decision Trees, co-authored with M. Aymen Bouyahia, 2026 [pdf][code].
  • Enhancing Exploration in Global Optimization by Noise Injection in the Probability Measures Space, co-authored with G. Serré, P. Germain, and S. Gruffaz, 2026. [pdf]
  • Cascaded Transfer: Learning Many Tasks under Budget Constraints, co-authored with E. Campagne, Y. Amara-Ouali, Y. Goude, and M. Mougeot, 2026. [pdf]
  • Reducing Recurrent Competitive Epidemics via Dynamic Resource Allocation, co-authored with G. Abel, and S. Sarao-Mannelli, 2025. [pdf]
  • Multi-view diffusion geometry using intertwined diffusion trajectories, co-authored with G. Debaussart-Joniec, 2025. [pdf][code]
  • Formal equivalence between global optimization consistency and random search, co-authored with G. Serré and N. Vayatis, 2025. [pdf][Lean code]
• ARCOM conf.: Gaspard Abel presented our work on multivariate Hawkes processes for social interactions and visualization at ARCOM’s event (co-organized with ENS P.S.). His talk had the title Analyse multi-échelle de la diffusion d’informations dans les réseaux sociaux [read more].
• PhD position: We are actively looking to fill an open PhD position on online change-point detection for structured multivariate time series. Read more.
• Preprint: the new preprint Graph Neural Networks for Electricity Load Forecasting consolidates two works that has been earlier presented in two ECML Workshops (2024 & 2025). Co-authored with E. Campagne, Y. Amara-Ouali, Y. Goude, and I. Zehavi. [pdf][code]
• Code: The GraphToolbox is a Python package designed for graph machine learning focused on time-series forecasting. It provides tools for data handling, model building, training, evaluation, and visualization. Developed as part of the work of our PhD student Eloi Campagne.
• JMLR: The paper Collaborative likelihood-ratio estimation over graphs, co-authored with A. de la Concha and N. Vayatis has been accepted in the Journal of Machine Learning Research. [pdf][code]
• New pre-doc: Gabriel Singer has started a pre-doc with us and in collaboration with SNCF (in the frame of the IDAML Chair).
• New intern:
  • Mohamed Ben Loughmari (M1 ENSTA Paris) on advanced learning techniques for temporal point processes (Oct–Dec).
• Pattern Recognition: The paper UniForCE: The unimodality forest method for clustering and estimation of the number of clusters, co-authored with G. Vardakas and A. Likas, has been accepted to the Pattern Recognition journal (Elsevier) and will appear soon. [pdf][code]
• ECML 2025: the paper Plugging Attention Matrices into Power Grids: Towards Transparent Forecasting, co-authored along with E. Campagne, I. Zehavi, Y. Amara-Ouali, and Y. Goude, will appear at the ECML 2025 Workshop on Machine Learning for Sustainable Energy Systems (ML4SPS). [pdf]

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