Florence d'Alché-Buc

Member of LTCI, Dept Image Data and Signal, S2A Team

Head of the Image, Data and Signal Department at Télécom Paris – started in september 2021.

Holder of the Télécom Paris Chair about Data Science and Artificial Intelligence for digitalised industry and services,
Funded by industrial partners: Airbus Defense and Space, ENGIE, IDEMIA, SAFRAN, VALEO – Started in 2019.

Formerly head of DigiCosme, the ANR Excellence laboratory in Computer Science of Paris-Saclay University

Research Interests
Machine Learning & Artificial Intelligence, bioinformatics & medical applications, industrial applications.
Subdomains: (Operator-valued) Kernel Methods, Structured Output Prediction, Complex data, Reliable Machine Learning, Dynamical Systems Modeling.

Occasional collaborator of CMAP, Ecole Polytechnique.

Recent services
IEEE Tr. PAMI Associate editor

JMLR action editor .

Senior Area Chair for ICML 2022, Neurips 2021

Member of Advising board of IVADO (Montreal, Canada), Co-program chair at NeurIPS 2019 , Senior Area chair at Neurips 2018, Member of program committee of Data Summer School, Palaiseau, May 28 – June 1, 2018


  • MAY 15 2022: We have 2 papers @ ICML 2022: “Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters”, Luc Brogat-Motte, Rémi Flamary, Céline Brouard, Juho Rousu + “Functional Output Regression with Infimal Convolution: Exploring the Huber and epsilon-insensitive Losses”, With Alex Lambert, Dimitri Bouche and Zoltan Szabò.

Colloquium @ ENS, April 8, 2022.

Keynote @ LIKE22: January 2022

Keynote at Mathias 2021

Keynote paper at CAP 2021: Structured prediction: a kernel view

Our papers: Nonlinear Functional Output Regression: A Dictionary Approach and When OT meets MoM: Robust estimation of Wasserstein Distance are accepted at AISTATS 2021.

Tutorial: Kernels: shallow and deep lerning, Data Science Summer School, Jan 6-10 2021

Keynote address: Kernel-based Structured Output Prediction for Metabolite Identification, Machine Learning for Science and Engineering Conference Dec 14-15, 2020, Columbia University.

our paper Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses accepted @ ICML 2020our paper Audio-Based Detection of Explicit Content in Music accepted @ ICASSP 2020


  • Talk @ Parietal, INRIA, July 21, 2020
  • AI & Genomics: talk at Colloque Génomique Numérique, nov 21, 2019.
  • our ACML paper about Functional Isolation Forest to be presented by Guillaume Staerman in Nagoya, November 19-21, 2019
  • our EMLNP paper to be presented by Pierre Colombo in Hong-Kong, November 3-7, 2019
    – Talk about AI, Sept 16, Airbus, Paris, 2019.
    – Talk about AI, March 27, Académie de Versailles, Journée ISN, INRIA Rocquencourt, 2019
    – Jan 2019: Our papers “Infinite task Learning with RKHSs” with Romain Brault and Alex Lambert (first authors) and Zoltan Szabo and Maxime Sangnier, and “Autoencoding any data with kernel autoencoders” with Pierre Laforgue (first author) and Stephan Clémençon just accepted at AISTATS 2019
    – Talk at Horizon Maths about AI and ML, Nov 23, 2018.
    – Talk at Journées France-IA, Oct 17, 2018.
    – Member of Programme Committee of Workshop on Machine Learning and Explainability, Orléans, Oct 8.
    – Meetup Women in Machine Learning, Sept 26, 2018.
    – Talk at Observatoire AAF du digital, Sept 19, 2018: Artificial Intelligence and Machine Learning, successes and challenges.
    – Sept 18, 2018: Our paper “Temporal clustering analysis of endothelial cell gene expression following exposure to a conventional radiotherapy dose fraction using Gaussian process clustering” with Markus Heinonen and Olivier Guipaud and colleagues from IRSN has just been accepted to Plos one.
    – Organizing Int. Workshop on Machine Learning and AI, Chair Machine Learning for Big Data, Télécom Paris, Paris, 17-18 Sept 2018.
    – Member of program committee of 3rd Junior Conference on Data Science and Engineering, Orsay, 13-14 Sept 2018.
    – Talk at Workshop on Gaussian Processes, Sheffield University, Sept 6, 2018.
    – Our paper A structured Prediction Approach for Label Ranking accepted at NIPS 2018.
    – Déjeuner scientifique aux Journées Statistiques 2018 (31 Mai 2018)
    – Our paper on Structured Output Learning with Abstention, accepted at ICML 2018.
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© 2020 Florence d'Alché-Buc