Hugo Gangloff 😌 😎

I am a research engineer at INRAE, MIA Paris-Saclay, SOLsTIS team.

Contact: hugo.gangloff(at)inrae(dot)fr

Code and resources

Continuous Normalizing Flows

• Tutorial [notes] [notebook]

Physics Informed Neural Networks

• Physics Informed Neural Networks with JAX: jinns Python package

Deep Gaussian Markov Random Fields

• DGMRFs and their extensions: dgmrf Python package

Variational Autoencoders

• Reparametrization trick tutorial [slides]
• VAE and VAE-GRF for anomaly detection [code]
• Tutorial session on Variational Autoencoders for scRNA-seq data for DIGIT-BIO seminar [slides]

Supervising

Master students

• Jules Brablé (with Tristan Mary-Huard, Julie Aubert and José Gomez-Garcia) on the project: Application de l’IA à la prédiction des performances de bovins sur la base de spectres infra-rouge, May-September 2023.
• Lucie-Garance Barot (with Marie-Laure Martin and Yacine Djabali) on the project: Study of the drought stress response among a maize panel, November-April 2023.
• Tanya Singh (with Minh-Tan Pham and Stefan Lang) on the project: Object counting without detection: Application to marine animal monitoring from aerial imagery, January-July 2022.
• Marwa Khaskhoussi (with Christophe Collet) on the project: Traitement de l'image et des données pour l'étude des calcifications de la bifurcation aortique, May-September 2020.
• Hanane Naamoune (with Salomé Kuntz and Nabil Chakfé) on the project: Segmentation des composants histologiques d'explants de chirurgie vasculaire par apprentissage profond, January-June 2020.

Publications

Preprints

Journal articles

• Self-supervised Variational Autoencoder for Unsupervised Object Counting from Very High-Resolution Satellite Imagery: Applications in Dwelling Extraction in FDP Settlement Areas, G.W. Gella, H. Gangloff, L. Wendt, D. Tiede, S. Lang, IEEE Transactions on Geoscience and Remote Sensing, 2023, [link]
• Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data, H. Gangloff, K. Morales, Y. Petetin, Computational Statistics and Data Analysis, 2022, [preprint pdf] [link] [code]
• Unsupervised segmentation with Gaussian Pairwise Markov Fields, H. Gangloff, J.-B. Courbot, E. Monfrini, C. Collet, Computational Statistics and Data Analysis, 2021, [preprint pdf] [link]
• Automated histological segmentation on micro-computed tomography images, S. Kuntz, H. Gangloff, H. Naamoune, A. Lejay, R. Virmani, N. Chakfé, European Journal of Vascular & Endovascular Surgery, 2021, [link]
• Co-registration of peripheral atherosclerotic plaques assessed by conventional CT-angiography, micro-CT and histology in CLTI patients, S. Kuntz, H. Jinnouchi, M. Kutyna, S. Torii, A. Cornelissen, Y. Sato, M. E. Romero, F. Kolodgie, A. V. Finn, A. Schwein, M. Ohana, H. Gangloff, A. Lejay, N. Chakfé, R. Virmani, European Journal of Vascular & Endovascular Surgery, 2020, [link]
• Assessing the segmentation performance of pairwise and triplet Markov models, I. Gorynin, H. Gangloff, E. Monfrini, W. Pieczynski, Signal Processing, 2017, [pdf] [link]

International conference articles

• Unsupervised Anomaly Detection Using Variational Autoencoders With Gaussian Random Field Prior, H. Gangloff, M.-T. Pham, L. Courtrai, S. Lefèvre, International Conference on Image Processing (ICIP), 2023, [preprint pdf], [code]
• Variational Autoencoders for unsupervised object counting from VHR imagery: applications in dwelling extraction from IDP/refugee settlements, G.W. Gella, H. Gangloff, L. Wendt, D. Tiede, S. Lang, International Geoscience and Remote Sensing Symposium (IGARSS), 2023.
• Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder, M.T. Pham, H. Gangloff, S. Lefèvre, International Geoscience and Remote Sensing Symposium (IGARSS), 2023.
• Object counting from aerial remote sensing images: application to wildlife and marine mammals, T. Singh, H. Gangloff, M.T. Pham, International Geoscience and Remote Sensing Symposium (IGARSS), 2023.
• Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection, H. Gangloff, M.-T. Pham, L. Courtrai, S. Lefèvre, International Conference on Pattern Recognition (ICPR), 2022, [preprint pdf]
• A general parametrization framework for Pairwise Markov Models: an application to unsupervised image segmentation, H. Gangloff, K. Morales, Y. Petetin, International Workshop on Machine Learning for Signal Processing (MLSP), 2021, [preprint pdf], [link]
• Unsupervised segmentation with Spatial Triplet Markov Trees, H. Gangloff, J-B Courbot, E. Monfrini, C. Collet, International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, [link], [slides], [poster]
• Improved centerline tracking for new descriptors of atherosclerotic aortas, H. Gangloff, E. Monfrini, M. Z. Ghariani, M. Ohana, C. Collet, N. Chakfé, International Conference on Image Processing Theory, Tools and Applications (IPTA), 2020, [preprint pdf]
• Unsupervised segmentation of stents corrupted by artifacts in medical X-rays images, H. Gangloff, E. Monfrini, C. Collet, N. Chakfé, International Conference on Image Processing Theory, Tools and Applications (IPTA), 2020, [preprint pdf]
• Spatial Triplet Markov Trees for auxiliary variational inference in Spatial Bayes Networks, H. Gangloff, J-B Courbot, E. Monfrini, C. Collet, Stochastic Modeling Techniques and Data Analysis (SMTDA), 2020, [preprint pdf], [slides]
• Performance comparison across hidden, pairwise and triplet Markov models' estimators, I. Gorynin, L. Crelier, H. Gangloff, E. Monfrini, W. Pieczynski, International Conference on Applied and Computational Mathematics (ICACM) , 2016

French conference articles

• Study of the drought stress response among a maize panel, L.-G. Barot, H. Gangloff, Y. Djabali, M. Blein-Nicolas, M.-L. Martin, Congrès Junior Pluridisciplinaire de l'Université Paris-Saclay, 2023
• Chaînes de Markov cachées à bruit généralisé, H. Gangloff, K. Morales, Y. Petetin, GRETSI, 2022, [preprint pdf] (une présentation alternative des chaînes de Markov triplet généralisées)
• Autoencodeurs variationnels à registre de vecteurs pour la détection d’anomalies, H. Gangloff, M.-T. Pham, L. Courtrai, S. Lefèvre, Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP), 2022, [pdf] (adaptation française de l'article ICPR 2022)
• Segmentation non-supervisée dans les champs de Markov couples gaussiens, H. Gangloff, J-B Courbot, E. Monfrini, C. Collet, GRETSI, 2019
• Segmentation de stents dans des données médicales à rayons-X corrompues par les artéfacts, H. Gangloff, E. Monfrini, C. Collet, N. Chakfe, GRETSI, 2019

See also abstract selected publications.

PhD Thesis

• Probabilistic models for image processing: applications in vascular surgery, H. Gangloff, defended on December 15, 2020, [pdf] [slides]

Awards

• Regional award André Blanc-Lapierre from the Electrity, Electronics and Information Scommunication Technology Society (SEE), in 2017, for the research project Stent segmentation and classification in superficial femoral artery in microCT images.
• Second Prize for the best research internship from Mines-Télécom Foundation, in 2017, for the research project Stent segmentation and classification in superficial femoral artery in microCT images.

Teaching

See previous teaching experiences here.

About me

My research interests are:

• Hidden Markov models and Bayesian inference
• Statistical signal processing
• Machine learning, deep learning
• Solving problems in medical and environmental sciences

Between 2020 and 2022, I was a postdoctoral researcher at Télécom SudParis and IRISA. I worked on unsupervised statistical image segmentation approaches and unsupervised anomaly detection. Between 2017 and 2020, I was a PhD student working at Geprovas and ICube. I worked on developping new tools for the processing of images from vascular surgery. Before that, I graduated from the French engineering school Télécom SudParis in 2017.