Professor, Polytech Annecy-Chambéry.
Contact.
Email : alexandre.benoit –@– univ-smb.fr
Phone : +33(0) 4 50 09 65 22 – télécopie : +33(0) 4 50 09 65 22
Office : A223
Postal address : LISTIC – Polytech Annecy Chambéry, BP 80439, 74944 Annecy le Vieux Cedex, France
Position / responsabilities.
- Permanent LISTIC Lab. member, professor position
 - Main teaching activities at Polytech Annecy Chambéry, Université Savoie Mont Blanc.
 
Research interests.
    Member of the « Apprentissage, Fusion et Télédétection (AFuTé) thematic » with strong connexions with ReGaRD thematics on distributed systems, HPC, data and ethics,
- Image, video and more generally data understanding : from data preprocessing, features extraction to concept recognition (objects, action, etc.) to classical 1D sensors.
 - Heterogeneous Data Fusion : associating multimodal inputs, images, contextual indicators, and others to make the decision process more reliable.
 - Sensor time series in general, from image sequences (multimedia, satellite remote sensing, astrophysics imaging) to classical sensors analysis.
 - Current methodological approaches rely on deep learning methods with some specific directions:
- Frugal models, low computational cost by design: light architectures efficient in terms of training efficacy as well as reduced inference time.
 - Explainable models, with two approaches:
- physical aware models design : by introducing physical knowledge in the model structure or by introducing optimization criteria related to the physical phenomenon to be considered.
 - developing XAI visualization techniques to help explain and understand model behaviors and predictions.
 
 - Physically informed models, ongoing activity
 - Federated learning, distributed and privacy preserving machine learning, ongoing activity.
 
 
Keywords
Video clustering Classification Retina modelDeep Learning Semi-supervised learning 3D convolutional neural network V1 cortex model Semantic Segmentation Real-time retina Human vision Remote sensingSpectral Clustering 2D metrics 3DOpenInterface Deep learning Attention level, Pairwise constraints Graph Cut
Publications
An overview of my publications is provided here : https://cv.archives-ouvertes.fr/alexandre-benoit and here also (subject to bugs) : registered in HAL
Research Projects
- A brief overview on these slides
 - Current projects:
- Gamma rays classification and parameters estimation.
- Keywords : deep learning, astrophysics, model inversion, physics inspired models, domain adaptation, multitask learning, classification, regression.
 - Academic partner: LAPP
 - Industrial partner : Orobix
 
 - Federated Learning and bias mitigation
- Keywords : deep learning, decentralized learning, model and data bias detection, clients clustering
 - Academic and industrial research
 
 - Deep learning for Alpin glacier model inversion
- Keywords : deep learning, model inversion, physics inspired models
 
 - IoT and AI for health care
- Keywords : deep learning, time series analysis
 - internal long run project at LISTIC
 
 
 - Gamma rays classification and parameters estimation.
 - Recent projects:
- Oil slicks detection and classification.
- Keywords : deep learning, SAR data analysis, heterogeneous data fusion, anomaly detection, semantic segmentation, classification.
 - Industrial partner : Total
 
 - Object detection and recognition for office inventory.
- Keywords : deep learning, 360° images, 3D scene modelling.
 - Industrial partner : SmarterPlan.io
 
 
 - Oil slicks detection and classification.
 
Teaching :
- Related to my research activities:
- Image and data processing with tools such as Matlab, and Python, OpenCV
 - Deep larning with Tensorflow.
 - Target students : PhD, Master and Bachelor degree and employed engineers.
 
 - General teaching activities:
- Programming : scripting and object programming with C/C++/Java/Python/PHP, and others.
 - Modeling (UML, etc.)
 
 - Experience highlights :
- image processing and machine learning to PhD students and Master 2 at University Savoie Mont Blanc.
 - machine learning at ESEN engineering school at Tunis (Tunisia)
 - machine learning at Renault compagny (Paris, Versailles)
 
 
Consulting :
- Introducing machine learning in digital processes in the industry.
 - Data pre-processing, features extraction and decision making for classification, regression and other tasks for image, video and time series understanding.
 - Experience highlights :
- Machine Learning and Deep Learning at Renault (Paris, Versailles)
 - Deep Learning at Total company(Paris)
 - Machine Learning at AboutGoods Company and Inventhys (Annecy)
 - Machine Learning with startups (Heliocity, Skiply and so on) along implications into the IDEFICS project
 - AI project planning with Concentrix company
 
 
PhD thesis co-supervising :
Current PhDs:
- 2024-2028 François-Xavier Sikoumno
 
Topic : Decentralised federated deep learning, anomaly detection, multitemporal time series.
- 2022-2025 Lorenzo Lopez-Uroz
 
Topic : Deep learning, model inversion, physics inspired models.
- 2022-2025 Lynda Ferraguyg
 
Topic : Féderated learning, bias detection and mitigation
- 2021-2024 Michael Dell’Aiera
Topic : Semi supervised Deep neural networks for gamma rays analysis, physics inspired models..
Funding : H2020 ESCAPE project and LAPP lab collaboration. 
Defended PhDs:
- 2018-2021 Emna Amri
Topic : Automatic Oil Slicks detection and classification from Remote Sensing imaging using Deep Learning.
Funding : Total company. - 2017-2020 Mickael Jacquemont
Topic : Deep neural networks for the 3D analysis of gamma rays.
Funding : H2020 ASTERICS project and the USMB fondation. - 2015-2019 Amina Ben Hamida
Topic : Indexing of large Remote sensing images.
Funding : French research ministry. - 2014-2019 Rizlène Raoui
Topic : Automatic Sales receipt understanding using Deep Learing.
Funding : CIFRE with AboutGoods Compagny. - 2013-2016 Nicolas Voiron
Topic : Automating clustering of multimedia data bases for exploration.
Funding : USMB - 2010-2013 Sabin Tiberius Strat
Topic : Automating understanding of image sequences using a collaborative approach
Funding : French research ministry 
Conference/Workshop organization
- 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images, July 22-24, 2015 Annecy, France
 - 2012 10th Workshop on Content-Based Multimedia Indexing (CBMI2012), 27-29 juin 2012, Annecy le Vieux, France.
 - 2011 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2011) July 18-22, 2011, jointly with Les Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2011, aka French Days on Fuzzy Logic and Applications) in Aix-les-Bains, France.
 
Reading committee
Journals:
IEEE Transactions on Image Processing ; Elsevier Computer Vision and Image Understanding ; Elsevier Signal Processing :Image Communication ; Multimedia Tools and APplications (MTAP) ; International Journal of Neural Systems ; MDPI Open Access Journal – Remote Sensing ; Hindawi – JournalOfRobotics ; ISPRS Journal of Photogrammetry and Remote Sensing ; JSTARS – GRSS | IEEE | Geoscience & Remote Sensing Society ; Integrated Computer-Aided Engineering
Conferences:
Main ones : IEEE International Conference on Image Processing ; International Workshop on Content-Based Multimedia Indexing ; JURSE – Joint Urban Remote Sensing Event ;
Scientific animation
2018 – 2019 co coordination of the MAIM at GDR ISIS.
2015 – 2017 co coordination of the IRIM at GDR ISIS..
Codes :
- a bio-inspired retina model for image pre-processing. contributed to the OpenCV library (C++/Python):
 - Deep Learning frameworks and related tools :
- pyTorch based framework to conduct versioned experiments, codes&docs. This is developed and applied in the context of a phD focused on gamma rays detection and analysis in the context of the internationnal Cherenkov Telescope Array project. The proposed framework can be applied to any image processing problem.
 - pyTorch indexed convolutions than enable any kind of kernels and pixel sampling grids : doc. This is applied to hexagonal images processing in the Cherenkov Telescope Array project.
 - Tensorflow based framework to conduct versioned experiments, codes&docs. Several examples provided for image segmentation, curves regression, GAN, etc.
 - caffe based deep learning model for hyperspectral images analysis using lightweight 3D convolutions based networks codes, paper