Riccardo Finotello Ingénieur chercheur

Identifiants chercheurs

IdHAL :

riccardo-finotello

ORCID :

0000-0002-8472-9004

arXiv :

finotello_r_1

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 and data science. The first is the analysis of hyperspectral images (often issued from spectroscopy techniques, such as LIBS or NIR 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 for computer vision: I study the properties of hyperspectral images using graph neural networks 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 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.

Publications

Article dans une revue (10)

Functional renormalization group for signal detection and stochastic ergodicity breaking Harold Erbin,Riccardo Finotello,Bio Wahabou Kpera,Vincent Lahoche,Dine Ousmane Samary Journal of Statistical Mechanics: Theory and Experiment, 2024, 2024 (8), pp.083203. ⟨10.1088/1742-5468/ad5c5c⟩ Article dans une revue hal-04256551v1
Interlaboratory comparison for quantitative chlorine analysis in cement pastes with laser induced breakdown spectroscopy Tobias Völker,Gerd Wilsch,Igor B. Gornushkin,Lucie Kratochvilova,Pavel Porizkaet al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2023, 202, pp.106632. ⟨10.1016/j.sab.2023.106632⟩ Article dans une revue cea-04466642v1
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 Tamaazoustiet al. EPJ Web of Conferences, 2023, 288, pp.01005. ⟨10.1051/epjconf/202328801005⟩ Article dans une revue cea-04351411v1
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
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
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
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
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
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

Communication dans un congrès (1)

Analyse élémentaire et isotopique par ablation laser et spectroscopie optique Jean-Baptiste Sirven,Daniel L'HERMITE,Céline Quere,Doriane Gallot-Duval,Kévin Touchetet al. Séminaire CETAMA Analyse élémentaire et isotopique en surface/sub-surface, Oct 2023, Pau, France Communication dans un congrès cea-04288522v1

Poster de conférence (1)

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

Chapitre d'ouvrage (1)

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