Keywords
Team
STIM
Biography
Bruno Figliuzzi is a researcher whose work lies at the intersection of materials science, signal processing, and machine learning. His research focuses primarily on developing advanced methods for analyzing and modeling complex microstructures, particularly in industrial materials such as steels, reinforced polymers, and metal coatings. He explores innovative approaches in hyperspectral imaging, image segmentation, and stochastic modeling to characterize physical phenomena such as selective oxide formation, charge percolation in composites, and the electrical properties of conductive coatings. His contributions also include work on optimal transport, domain generalization for time series, and Bayesian optimization for the identification of morphological parameters. His methods, often validated by numerical and experimental studies, find applications in a variety of sectors, ranging from aerospace to metallurgy, including industrial defect detection.
Publication(s)
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2025
Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction
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2024
Optimal Transport Based Hyperspectral Unmixing for Highly Mixed Observations DOI : 10.1109/WHISPERS65427.2024.10876524
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2023
Surface oxides characterization based on hyperspectral observations DOI : 10.1016/j.chemolab.2023.104879
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2022
A Supervised Approach for the Detection of Surface Oxides from Hyperspectral Measurements DOI : 10.1109/WHISPERS56178.2022.9955061
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2022
Surface Oxide Detection and Characterization Using Sparse Unmixing on Hyperspectral Images DOI : 10.1007/978-3-031-13321-3_26
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2022
Characterization of Surface Oxides from Hyperspectral Measurements DOI : 10.1109/WHISPERS56178.2022.9955055
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2021
A BAYESIAN APPROACH TO MORPHOLOGICAL MODELS CHARACTERIZATION DOI : 10.5566/IAS.2641
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2020
Electrical conductivity of metal-polymer cold spray composite coatings onto carbon fiber-reinforced polymer
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2020
Electrical Conductivity of Metal–Polymer Cold Spray Composite Coatings onto Carbon Fiber-Reinforced Polymer DOI : 10.1007/s11666-020-00999-7
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2020
Fast macula detection and application to retinal image quality assessment DOI : 10.1016/j.bspc.2019.101567
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2019
Fast marching based superpixels generation DOI : 10.1007/978-3-030-20867-7_27
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2019
Eikonal-based models of random tessellations DOI : 10.5566/ias.2061
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2018
Morphological modeling of cold spray coatings DOI : 10.5566/ias.1894
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2018
New parameterizations for Bayesian seismic tomography DOI : 10.1088/1361-6420/aabce7
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2017
Function decomposition in main and lesser peaks DOI : 10.1007/978-3-319-57240-6_26
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2017
Automatic detection of cracks and delaminations in thermal images
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2017
Computational material design of filled rubbers using multi-objective design exploration DOI : 10.1201/9781315223278-85
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2017
Hierarchical segmentation based upon multi-resolution approximations and the watershed transform DOI : 10.1007/978-3-319-57240-6_15
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2016
Nonlinear electrophoresis in the presence of dielectric decrement DOI : 10.1103/PhysRevE.94.023115
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2016
Modelling the microstructure and the viscoelastic behaviour of carbon black filled rubber materials from 3D simulations
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2016
A Bayesian approach to linear unmixing in the presence of highly mixed spectra DOI : 10.1007/978-3-319-48680-2_24
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2016
Cold spray of metal-polymer composite coatings onto Carbon Fiber-Reinforced Polymer (CFRP)
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2015
A Fourier-based numerical homogenization tool for an explosive material DOI : 10.1051/mattech/2015019
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2014
Design of capillary flows with functionally graded porous titanium oxide films fabricated by anodization instability DOI : 10.1016/j.jcis.2014.02.032
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2014
Nonlinear electrophoresis of ideally polarizable particles DOI : 10.1063/1.4897262
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2014
Dielectric decrement effects on nonlinear electrophoresis of ideally polarizable particles
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2013
Rise in optimized capillary channels DOI : 10.1017/jfm.2013.373
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2012
Rheology of thin films from flow observations DOI : 10.1007/s00348-012-1359-4
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2012
Numerical simulation of thin paint film flow DOI : 10.1186/2190-5983-2-1
Teaching
Signal Processing
The Signal Processing course covers all aspects of harmonic analysis, including discrete- and continuous-time signals, convolution, operational calculus, and the Fourier transform. This content is presented in a way that makes it applicable in various contexts such as mathematics, engineering, and physics. The course establishes a fundamental link between the mathematical foundations of signal processing (Fourier analysis, wavelets, etc.) and the practical tools derived from them, such as filtering and compression. These concepts are illustrated by modern technologies that incorporate these principles, thereby providing a concrete and applied perspective on the theories covered. Furthermore, the course highlights the strong interconnections between signal processing and other fields covered by the Applied Mathematics course unit, naturally guiding instruction toward the fundamental mathematical concepts of the field. Practical exercises, designed to simply illustrate the theorems studied in class, reinforce this theoretical learning. Finally, the exploration of recent technological applications provides a current and dynamic perspective on the theoretical results presented. Independent study hours are devoted to projects on topics that go beyond the scope of the course. Recent projects have included the implementation of a music recognition algorithm based on the windowed Fourier transform, the study of signal processing tools used in tomography, and the application of signal processing tools to a geophysical problem.
Elective Course Period (October and January)
Digital Engineering of Complex Systems (IDSC) track
Over the past decade, artificial intelligence (AI) has profoundly transformed the industrial world by revolutionizing the methods used to design, optimize, and operate systems. One of the most striking aspects of this evolution is the digital modeling of complex systems. Traditionally based on methods with a strong physical or multiphysical focus, this field now benefits from advances in artificial intelligence, particularly through machine learning and deep learning algorithms. The Digital Engineering of Complex Systems track at Mines Paris aims to provide students with a solid, multidisciplinary foundation in AI, enabling them to gain a deep understanding of its applications and apply them to real-world industrial challenges. The disciplines studied in the specialization are: machine learning and its applications in engineering, computer vision, and high-performance computing. The specialization’s curriculum is structured around three weeks of coursework, supplemented by two weeks of a Data Challenge and a one-week field trip. More information is available on the track’s webpage: https://bruno-figliuzzi.github.io/Option/
PhD supervision
- 2024 Image analysis for lithology characterization ZEGHDOUD Raounek
- 2023 Telecommunication of geological layers using seismic waves AKLEH Christina
- 2023 Inverse problem solving using Deep Learning methods applied to hyperspectral images. DOUTSAS Delphine
- 2022 Data-Driven and Physics-Based Approaches for Stick-Slip Estimation YAHIA Hana
- 2021 Advanced Hemodynamic Modeling and Simulation of Flow-Diverter Stents in the Treatment of Intracranial Aneurysms JEKEN RICO Pablo
- 2019 Characterization of the physicochemical properties of steel surfaces by hyperspectral imaging. ZENATI Tarek
- 2016 Artificial learning for image segmentation CHANG Kaiwen
- 2015 Image segmentation problems and contribution to mathematical morphology ALAIS Robin