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Riccardo Finotello

Ingénieur chercheur
13
Documents
Affiliations actuelles
  • 1182548
Identifiants chercheurs
  • IdHAL riccardo-finotello
  • Arxiv : finotello_r_1
  • Google Scholar : https://scholar.google.fr/citations?user=mT3o06QAAAAJ

Présentation

My research interests cover physical and computational problems, the common thread being the relation between applied mathematics and artificial intelligence, from data acquisition to the analysis. At present, I focus on two principal research areas, related to [computer vision](https://en.wikipedia.org/wiki/Computer_vision) and data science. The first is the analysis of hyperspectral images (often issued from spectroscopy techniques, such as [LIBS](https://en.wikipedia.org/wiki/Laser-induced_breakdown_spectroscopy) or [NIR](http://www.scholarpedia.org/article/Near_infrared_imaging) imagery) using supervised and unsupervised methods for object detection and (panoptic) segmentation of scenes. In particular, I am interested in geometric deep learning and [representation learning](https://paperswithcode.com/task/representation-learning) for computer vision: I study the properties of hyperspectral images using [graph neural networks](https://en.wikipedia.org/wiki/Graph_neural_network) and geometry, in order to recover the full extent of the information present in the images.The other is the application of ML and AI to experimental physics. Specifically, I took an interest in [explainable AI](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence) methods and the definition of uncertainties in deep learning. I actively work in the development of deep learning techniques capable of characterize each measurement, in order to exploit statistical models in order to reject outliers and to provide a measure of the uncertainty of the prediction. I am also interested in applications of machine and deep learning to the theory of mathematics and physics, such as algebraic geometry and string theory, for their fascinating structures and their ability to provide geometrical insights on the behaviour of neural network architectures.

Domaines de recherche

Intelligence artificielle [cs.AI] Apprentissage [cs.LG] Physique des Hautes Energies - Théorie [hep-th] Machine Learning [stat.ML]

Compétences

Machine Learning Data Science Computer Vision Spectroscopy Chemometrics Geometry Field Theory

Publications

Image document

Trustworthiness of laser-induced breakdown spectroscopy predictions via simulation-based synthetic data augmentation and multitask learning

Riccardo Finotello , Daniel L'Hermite , Céline Quéré , Benjamin Rouge , Mohamed Tamaazousti
EPJ Web of Conferences, 2023, 288, pp.01005. ⟨10.1051/epjconf/202328801005⟩
Article dans une revue cea-04351411v1
Image document

Interlaboratory comparison for quantitative chlorine analysis in cement pastes with laser induced breakdown spectroscopy

Tobias Völker , Gerd Wilsch , Igor B. Gornushkin , Lucie Kratochvilova , Pavel Porizka
Spectrochimica Acta Part B: Atomic Spectroscopy, 2023, 202, pp.106632. ⟨10.1016/j.sab.2023.106632⟩
Article dans une revue cea-04466642v1
Image document

Deep multi-task mining Calabi-Yau four-folds

Harold Erbin , Riccardo Finotello , Robin Schneider , Mohamed Tamaazousti
Mach.Learn.Sci.Tech., 2022, 3 (1), pp.015006. ⟨10.1088/2632-2153/ac37f7⟩
Article dans une revue hal-03323024v1
Image document

HyperPCA: a powerful tool to extract elemental maps from noisy data obtained in LIBS mapping of materials

Riccardo Finotello , Mohamed Tamaazousti , Jean-Baptiste Sirven
Spectrochimica Acta Part B: Atomic Spectroscopy, 2022, 192, pp.106418. ⟨10.1016/j.sab.2022.106418⟩
Article dans une revue cea-03716823v1

Machine learning for complete intersection Calabi-Yau manifolds: A methodological study

Harold Erbin , Riccardo Finotello
Physical Review D, 2021, 103 (12), ⟨10.1103/PhysRevD.103.126014⟩
Article dans une revue hal-04351377v1

2D fermion on the strip with boundary defects as a CFT with excited spin fields

Riccardo Finotello , Igor Pesando
Nuclear Physics B, 2021, 969, pp.115464. ⟨10.1016/j.nuclphysb.2021.115464⟩
Article dans une revue hal-04351329v1
Image document

Inception neural network for complete intersection Calabi-Yau 3-folds

Harold Erbin , Riccardo Finotello
Machine Learning: Science and Technology, 2021, 2 (2), pp.02LT03. ⟨10.1088/2632-2153/abda61⟩
Article dans une revue cea-04082047v1

On the origin of divergences in time-dependent orbifolds

Andrea Arduino , Riccardo Finotello , Igor Pesando
European Physical Journal C: Particles and Fields, 2020, 80 (5), ⟨10.1140/epjc/s10052-020-8010-y⟩
Article dans une revue hal-04351342v1

The classical solution for the Bosonic string in the presence of three D-branes rotated by arbitrary SO(4) elements

Riccardo Finotello , Igor Pesando
Nuclear Physics B, 2019, 941, pp.158-194. ⟨10.1016/j.nuclphysb.2019.02.010⟩
Article dans une revue hal-04351305v1

Analyse élémentaire et isotopique par ablation laser et spectroscopie optique

Jean-Baptiste Sirven , Daniel L'HERMITE , Céline Quere , Doriane Gallot-Duval , Kévin Touchet
Séminaire CETAMA Analyse élémentaire et isotopique en surface/sub-surface, Oct 2023, Pau, France
Communication dans un congrès cea-04288522v1
Image document

Machine learning for complete intersection Calabi-Yau manifolds

Harold Erbin , Riccardo Finotello , Mohamed Tamaazousti
NeurIPS 2022 - The 36th conference on Neural Information Processing Systems, Dec 2022, New Orléans, United States. , 2022
Poster de conférence cea-04082321v1
Image document

Deep Learning: Complete Intersection Calabi–Yau Manifolds (Chapter 5)

Harold Erbin , Riccardo Finotello
Machine Learning in Pure Mathematics and Theoretical Physics, World Scientific, pp.151-181, 2023, 978-1-80061-369-0. ⟨10.1142/9781800613706_0005⟩
Chapitre d'ouvrage cea-04187820v1