Bio
I am currently Maître de Conférences of Statistics and Machine Learning at Université de Technologie de Compiègne (UTC). I teach in Computer Science Department and I do my research in laboratory of Applied Mathematics (LMAC).
My research focuses on theoretical, algorithmic, and practical aspects of machine learning. It lies at the intersection of statistics, optimization, and computer science: statistics provides the foundations for modeling and theoretical guarantees; optimization drives the design of efficient algorithms; and computer science enables scalable and reliable implementation. Together, these three disciplines form the core of modern data science.
I am also interested in developing machine/deep learning models and algorithms for interdisciplinary applications in mechanics, chemistry, biomedical, etc.
News
-
Jun. 2026
Interdisciplinary Paper, Composites Multi-source Sensor Data Fusion Framework for Structural Health Monitoring of Polymer-Matrix Composites (PMC) Based on Latent-Space Clustering Using a Convolutional Autoencoder. Mechanics of Advanced Materials and Structures.
-
Apr. 2026
Paper. Optimal Transport Convergence of Conditional Distribution Estimation for Single-Indexed Locally Stationary Functional Time Series. Results in Applied Mathematics.
-
Apr. 2026
Paper. Bounds in Wasserstein Distance for Locally Stationary Functional Time Series. Computational Statistics.
-
Mar. 2026
Spotlight Paper. Does Normalization Choice Matter for Causal Large Time-Series Models? ICLR 2026 Workshop on Time Series in the Age of Large Models.
- Feb. 2026
-
Jan. 2026
Paper. A Unified Kantorovich Duality for Multimarginal Optimal Transport. Submitted.
-
Jan. 2026
Paper. Optimal Transport Guarantees to Nonparametric Regression for Locally Stationary Time Series. Artificial Intelligence and Statistics Conference, AISTATS 2026.
-
Jan. 2026
Paper. Unmixing Mean Embeddings for Domain Adaptation with Target Label Proportion. Artificial Intelligence and Statistics Conference, AISTATS 2026.