Matthieu Dabrowski Post-doctoral researcher at IMT Nord Europe on human pose prediction from feet pressure values obtained through connected insoles. This work implied the realization of a dataset containing ground truths on human poses and feet pressure values.

Identifiants chercheurs

IdHAL :

matthieu-dabrowski

ORCID :

0009-0005-2391-3675

Présentation

My name is Matthieu Dabrowski, I am a french post-doctoral researcher in AI and Computer Vision.

I am mainly interested in projects related to Health or Environmental issues.

I have developed an expertise in Physics-Informed Neural Networks.

During my thesis this concept was applied to atmospheric physics.

I am currently working on biomechanics-informed models for human pose prediction from feet pressure data.

I am currently open for work and looking for a postdoctoral researcher position, starting from May 15th.

Publications

Article dans une revue (2)

Double-task physics-informed neural network for the prediction of PM2.5 concentration Matthieu Dabrowski,José Mennesson,Jérôme Riedi,Chaabane Djeraba Neurocomputing, 2026, pp.133436. ⟨10.1016/j.neucom.2026.133436⟩ Article dans une revue hal-05563223v1
Knowledge-inspired fusion strategies for the inference of PM 2.5 values with a neural network Matthieu Dabrowski,José Mennesson,Jérôme Riedi,Chaabane Djeraba,Pierre Nabat Geoscientific Model Development, 2025, 18 (12), pp.3707-3733. ⟨10.5194/gmd-18-3707-2025⟩ Article dans une revue hal-05128704v1

Communication dans un congrès (2)

HUMAPS-4D : A Multimodal Dataset for HUman Motion Analysis with Physiological and Semantic informations Matthieu Dabrowski,Ouala Ben Jemaa,Benjamin Allaert The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026, Jun 2026, Denver (Colorado), United States Communication dans un congrès hal-05531226v1
Semi-supervised GAN with sparse ground truth as Boundary Conditions Matthieu Dabrowski,José Mennesson,Jérôme Riedi,Chaabane Chabane Djeraba IEEE International Joint Conference on Neural Networks 2023, Jun 2023, Queensland, Australia Communication dans un congrès hal-04132474v1

Thèse (1)

Méthodes d'apprentissage profond inspirées par la physique pour l'inférence de la qualité de l'air Matthieu Dabrowski Apprentissage [cs.LG]. Université de Lille, 2024. Français. ⟨NNT : 2024ULILB043⟩ Thèse tel-05006008v1