Professor of Applied Mathematics

Bio

I am a professor in the Department of Applied Mathematics at École polytechnique, where I have been working since September 2013. My research is conducted at the Center for Applied Mathematics (CMAP). I am a member of the SIMPAS team (Signal Image Probability Numerical and Statistical Learning). I work on data-related problems using approaches related to machine learning, artificial intelligence, statistics, and signal processing. I have a passion for applications and for knowledge transfer through teaching. I teach both initial and continuing education courses. I am responsible for several programs offered by the school: MScT Data Science and Artificial Intelligence for Business and continuing education programs (AI for Business and Leading with Data and AI) for Polytechnique Executive Education. I also contributed to the creation of the M2 Data Science program at Institut Polytechnique de Paris and managed the PA for Applied Mathematics and Data Science for many years.

My current research focuses on two main areas: health (particularly women’s health) and reinforcement learning. I enjoy collaborating on these topics with PhD students and co-supervisors from diverse backgrounds.

Interests

Data Science and Artificial Intelligence
Statistics and Statistical Learning
Signal Processing
R and Python Data Science stack
Entrepreneurship
Health

Education

HdR, 2013

Université Paris Sud
PhD in Applied Mathematics, 2002

Ecole Polytechnique
Master in Mathematics and Artifical Intelligence, 1997

ENS Cachan

Publications

Selected publications from current research themes

“DeepCTG® 2.0: Development and validation of a deep learning model to detect neonatal acidemia from cardiotocography during labor” with I. Ben M’Barek, G. Jauvion, J.&nbps;Merrer, M. Koskas, O. Sibony, P.–F. Ceccaldi and J. Stirnemann, Computers in Biology and Medicine (2025)
“Wavelet-Based Multiscale Initial Flow For Improved Atlas Estimation in the Large Diffeomorphic Deformation Model Framework” with F. Gaudfernau, E. Blondiaux and S. Allassonière, Journal of Mathematical Imaging and Vision (2025)
“Near-Optimal Distributionally Robust RL with General Lp Norms” with P. Clavier, L. Shi, E. Mazumdar, A. Wierman and M. Geist , Neurips. (2024)
“Bootstrapping Expectiles in Reinforcement Learning” with P. Clavier, E. Rachelson and M. Geist, 7th European Workshop on Reinforcement Learning. (2024)
“State Abstraction Discovery from Progressive Disaggregation Methods” with O. Forghieri, H. Castel and E. Hyon, 7th European Workshop on Reinforcement Learning. (2024)
“Towards Minimax Optimality of Model-based Robust Reinforcement Learning” with P. Clavier and M. Geist, Conference on Uncertainty in Artificial Intelligence (UAI) (2024)
“Progressive State Space Disaggregation for Infinite Horizon Dynamic Programming” with O. Forghieri, H. Castel and E. Hyon, International Conference on Automated Planning and Scheduling (2024)
“A multiscale algorithm for computing realistic image transformations - Application to the modelling of fetal brain growth” with F. Gaudfernau and S. Allassonnière, SPIE Medical Imaging (2023)
“Input uncertainty propagation through trained neural networks” with P. Monchot, L. Coquelin, S. Petit, S. Marmin and N. Fischer, International Conference on Machine Learning (2023)
“Causal and Interpretable Rules for Time Series Analysis” avec A. Dhaou, A. Bertoncello, S. Gourvénec, et J. Garnier, Knowledge Discovery and Data Mining (2021)
" Intelligent Questionnaires Using Approximate Dynamic Programming" with F. Logé and H. Amadou-Boubacar, i-com (2021),

Last Preprints

“Bootstrapping Expectiles in Reinforcement Learning” with P. Clavier, E. Rachelson and M. Geist

For more details, see the research page or the comprehensive list of publications

Students

Current students

Guillaume Brochot, PhD in co-supervision with Thomas Clausen and Jean-Louis Rougier, 2024-, AI for network systems
David Soto, PhD in co-supervision with Ahmed Shokry and Alain Durmus, 2024-, CIFRE Nodewise (Vinci), MPC for room temperature
Imane Ben M’Barek, PhD in co-supervision with Julien Stirnemann, 2023-, Prediction of neonatal hypoxia by automated analysis of cardiotocography during labor
Orso Forghieri, PhD in co-supervision with Hind Castel and Emmanuel Hyon, 2022-, hierarchical reinforcement learning
Inass Kamal, PhD in co-supervision with Angelo Pierangelo, 2022-,Multispectal Mueller polarimetric imaging
Shadé Alao, PhD in co-supervision with Serge Cohen, 2022-, New statistical learning tools for imputation and prognosis in conservation science

Former students

Pierre Clavier, PhD in co-supervision with Stéphanie Allassonnière, 2021-2024, Robust Reinforcement Learning: Theory and Practice
Amin Dhaou, PhD in co-supervision with Josselin Garnier, 2021-2024, CIFRE TotalEnergie, Causality and Time Series
Fleur Gaudfernau, PhD in co-supervision with Stéphanie Allassonnière, 2020-2023, Diagnosis in IRM – Post Doc at Heka
Paul Monchot, PhD, 2020-2023, CIFRE LNE, Supervised learning and sensibility – R&D at LNE
Angie Pineda, Post Doc, 2018-2021, C’IL-LICO project, Health data – ENSAI
Thomas Galtier, Post Doc, 2019-2020, HealthChain project, Data Valuation, Federated Learning
Frédéric Logé-Munérel, PhD, 2017-2021, CIFRE Air Liquide, Planning and Reinforcement Learning – R&D at Sonio
Rémi Besson, PhD in co-supervision with Stéphanie Allassonnière, 2016-2019, Rare Disease Diagnosis Optimization – CSO at Sonio
Marco Brigham, Post Doc, 2017-2019, Morpheo project, EEG Analysis – CSO at Moonoia
Esther Derman, Associate Researcher, 2016-2017, Model Based Histogram Clustering – moved to Israel, PhD at the Technion, post-doc at MILA
Solenne Thivin, PhD, 2012-2015, CIFRE Thales Optronics, Detection on Complex Textures – professor in high school
Lucie Montuelle, PhD, 2011-2014, Conditional Density Estimation – Engineer at EDF
Joseph Salmon, PhD, 2007-2010, Estimator Aggregation – Full professor at Université de Montpellier