After a PhD in Computer Sciences from Jean Monnet University in 2004, Amaury Habrard taught as an assitant professor at Aix-Marseille University until 2011. He spent six months at the University of Alicante, Spain, to teach and do research. He got his Accreditation to lead Research (HDR) in 2010 and has been a Professor in Computer Science at Jean Monnet University since 2011.


Amaury Habrard is the coordinator of the MSc in Computer Science, since 2013 and the co-coordinator of the international master track on Machine Learning and Data Mining - MLDM, since 2017. He has been leading the Data Intelligence research team at the Laboratoire Hubert Curien since 2014 and has been a member of the Educational Committee of Manutech-SLEIGHT Graduate School since 2018. He is a member of the Scientific board of the Interdisciplinary Institute on Artificial Intelligence MIAI@GrenobleAlpes and the Educational board of MILyon: Excellence center in Mathematics and Theoretical Computer Science of the University of Lyon.


Amaury Habrard teaches courses in Machine Learning, pattern recognition, algorithmics and complexity, and programming at the Faculty and Science and Technology of Jean Monnet University.

  • Advanced Machine Learning
  • Machine Learning – Fundamentals and Algorithms
  • Deep Learning and Applications
  • Advanced Machine Learning
  • Advanced Algorithms and Programming
  • Design and Analysis of Algorithms
  • Imperative Programming

  • R. Viola, R. Emonet, A. Habrard, G. Metzler, M. Sebban: Learning from Few Positives: A Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data. Proc. of the International Joint Conference on Artificial Intelligence- IJCAI, 2020
  • P. Germain, A. Habrard, F. Laviolette, E. Morvant: PAC-Bayes and Domain Adaptation. Neurocomputing, vol. 379: 379-397 (2020)
  • G. Zaid, L. Bossuet, A. Habrard, A. Venelli: Methodology for Efficient CNN Architectures in Profiling Attacks. IACR Trans. Cryptogr. Hardw. Embed. Systems (TCHES) 2020(1): 1-36, 2020
  • I. Redko, E. Morvant, A. Habrard, M. Sebban and Y. Bennani: Advances in Domain Adaptation Theory. ISTE Press Ltd - Elsevier Inc, 208 pages, 2019.
  • J. Tissier, C. Gravier, A. Habrard: Near-Lossless Binarization of Word Embeddings. Proc. of the American Association for Artificial Intelligence- AAAI, 2019
  • N. Courty, R. Flamary, A. Habrard, A. Rakotomamonjy: Joint Distribution Optimal Transportation for Domain Adaptation. Proc. of the Annual Conference on Advances on Neural Information Processing Systems- NeurIPS, 2017
  • J. Fréry, A. Habrard, M.Sebban, O. Caelen, L. He-Guelton: Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection. Proc. of the European Conference on Machine Learning and Knowledge Discovery in Data Bases- ECML/PKDD, 2017

  • J. Frery, A. Habrard, M. Sebban, L. Guelton, O. Caelen. Detection by machine learning of anomalies in a set of banking transactions by optimization of the average precision, 2018. WO/2018/167404