From 2006 to 2008 I worked as a postdoctoral fellow at the Hebrew University of Jersualem in the department of statistics. Since 2008 I am a senior lecturer in the Shamoon College of Engineering, Israel. My main research interests include applied statistics, statistical signal processing, pattern recognition and machine learning with applications to spectroscopy and biomedical applications.
EDUCATION
Tom (Thomas) Trigano was born in Paris, France in 1978, and received an M.Sc. in engineering from the Telecom Paris Tech (France) and an M.Sc in Applied Probability from Paris VI University (France) in 2001. He recieved the Ph.D. degree in signal processing from the Telecom Paris Tech in 2005
Spectroscopic Pulse Embeddings by Contrastive Learning from Unlabeled Data for Pile-Up Analysis Congyu Lin, Xiaoying Zheng, Tom Trigano, Dima Bykhovsky, Yongxin Zhu, et al. Sensors, 2026 In nuclear spectroscopy, a physical phenomenon known as the pile-up effect distorts direct measurements by causing temporal overlap of detector pulses. Existing deep learning-based pile-up correction methods rely heavily on supervised training with simulated data, which often generalize poorly to real measurements due to simulation–experiment discrepancies. In this work, we propose a contrastive learning framework to learn robust and transferable representations directly from large-scale unlabeled real nuclear pulse signals. The detector output is segmented into physically complete pulse aggregations using a zero-crossing-based strategy, which serve as semantically coherent instances for representation learning. Physics-inspired data augmentations are designed to realistically model detector noise and bandwidth effects while preserving pulse area. A one-dimensional ResNet encoder is employed for efficient representation learning. The learned representations are transferred to pile-up identification and counting-rate estimation tasks. Experimental results on real nuclear radiation detection systems demonstrate that our method achieves strong performance and robustness under high counting-rate conditions, with particularly pronounced advantages in challenging peak pile-up scenarios.
Jensen–Tsallis divergence for supervised classification under data imbalance Antonio Squicciarini, Tom Trigano, David Luengo Machine Learning, 2025 In supervised classification problems using Deep Neural Networks, the loss function is typically based on the Kullback–Leibler divergence. However, alternative entropic divergence formulations, such as the Jensen–Shannon Divergence (JSD), have recently garnered attention for their unique properties. In this study, we delve deeper into the interpretation of the JSD and its generalized form, the Jensen–Tsallis Divergence (JTD), as alternative loss functions for supervised classification. When provided with one-hot encoded distributions for the true label probabilities, we demonstrate that these novel divergences impose an intrinsic output confidence regularization that prevents overfitting. Additionally, the q non-extensive parameter of the JTD directly influences the structure of the regularizer, offering increased flexibility in the formulation of the loss function. Through experiments conducted on artificially imbalanced versions of MNIST, Fashion-MNIST, SVHN and CIFAR-10 we showcase how JTD outperforms JSD and other traditional loss functions in terms of generalization performance, especially for highly imbalanced datasets.
Deep Learning Based Pile-Up Correction Algorithm for Spectrometric Data Under High-Count-Rate Measurements Yiwei Huang, Xiaoying Zheng, Yongxin Zhu, Tom Trigano, Dima Bykhovsky, et al. Sensors, 2025 Gamma-ray spectroscopy is essential in nuclear science, enabling the identification of radioactive materials through energy spectrum analysis. However, high count rates lead to pile-up effects, resulting in spectral distortions that hinder accurate isotope identification and activity estimation. This phenomenon highlights the need for automated and precise approaches to pile-up correction. We propose a novel deep learning (DL) framework plugging count rate information of pile-up signals with a 2D attention U-Net for energy spectrum recovery. The input to the model is an Energy–Duration matrix constructed from preprocessed pulse signals. Temporal and spatial features are jointly extracted, with count rate information embedded to enhance robustness under high count rate conditions. Training data were generated using an open-source simulator based on a public gamma spectrum database. The model’s performance was evaluated using Kullback–Leibler (KL) divergence, Mean Squared Error (MSE) Energy Resolution (ER), and Full Width at Half Maximum (FWHM). Results indicate that the proposed framework effectively predicts accurate spectra, minimizing errors even under severe pile-up effects. This work provides a robust framework for addressing pile-up effects in gamma-ray spectroscopy, presenting a practical solution for automated, high-accuracy spectrum estimation. The integration of temporal and spatial learning techniques offers promising prospects for advancing high-activity nuclear analysis applications.
Spectroscopic Pulse Embeddings by Contrastive Learning from Unlabeled Data for Pile-Up Analysis C Lin, X Zheng, T Trigano, D Bykhovsky, Y Zhu, L Tian Sensors 26 (7), 2138 , 2026 2026
Pre-and post-harvest spectral estimation of carnosic acid and rosmarinic acid in rosemary A Mishra, A Krief, MM Sahoo, A Schachter, I Gonda, N Dudai, T Trigano, ... Computers and Electronics in Agriculture 244, 111501 , 2026 2026
Doubly Stochastic Mean-Shift Clustering T Trigano, Y Sepulcre, I Lapidot arXiv preprint arXiv:2602.15393 , 2026 2026
Spectroscopic analysis reveals an opposite pattern between carnosic and rosmarinic acids concentration in rosemary (Salvia rosmarinus) A Mishra, A Krief, MM Sahoo, A Schachter, I Gonda, N Dudai, T Trigano, ... Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 127392 , 2025 2025
Stochastic mean-shift clustering I Lapidot, Y Sepulcre, T Trigano arXiv preprint arXiv:2511.09202 , 2025 2025 Citations: 3
FDP-TF: A fast two-pass trend filtering for ECG delineation T Trigano, Y Sepulcre, M Masika, A Perez, D Luengo Computers in Biology and Medicine 197, 110927 , 2025 2025 Citations: 1
Parallel implementation of spectral pileup correction and Gaussian noise suppression using CUDA heterogeneous architecture Z Chen, X Zheng, Y Zhu, T Trigano, Y Huang, Y Zhang Nuclear Instruments and Methods in Physics Research Section A: Accelerators … , 2025 2025
Jensen–Tsallis divergence for supervised classification under data imbalance A Squicciarini, T Trigano, D Luengo Machine Learning 114 (7), 162 , 2025 2025 Citations: 2
An open X-ray spectrometric dataset for deep learning-based pile-up correction C Lin, Z Chen, C Feng, S Gu, X Zheng, Y Zhu, T Trigano, D Bykhovsky International Conference on Wireless Artificial Intelligent Computing … , 2025 2025 Citations: 2
Deep Learning Based Energy Spectrum Estimation for High Counting Rate Nuclear Spectrometry Y Huang, C Lin, D Bykhovsky, T Trigano, Z Chen, X Zheng, Y Zhu IEEE Transactions on Instrumentation and Measurement , 2025 2025 Citations: 3
Deep learning based pile-up correction algorithm for spectrometric data under high-count-rate measurements Y Huang, X Zheng, Y Zhu, T Trigano, D Bykhovsky, Z Chen Sensors 25 (5), 1464 , 2025 2025 Citations: 3
Advanced spectroscopy time-domain signal simulator for the development of machine and deep learning algorithms D Bykhovsky, Z Chen, Y Huang, X Zheng, T Trigano IEEE Sensors Letters , 2025 2025 Citations: 3
GaSim: A python class to generate simulated time signals for gamma spectroscopy Z Chen, D Bykhovsky, X Zheng, T Trigano, Y Zhu SoftwareX 29, 102037 , 2025 2025 Citations: 3
Nanofilament organization in highly tough fibers based on lamin proteins Y Tzror, M Bezner, S Deri, T Trigano, K Ben-Harush Journal of the Mechanical Behavior of Biomedical Materials 160, 106748 , 2024 2024
Deep learning-based method for activity estimation from short-duration gamma spectroscopy recordings T Trigano, D Bykhovsky IEEE Transactions on Instrumentation and Measurement 73, 1-11 , 2024 2024 Citations: 7
Adaptive trend filtering for ECG denoising and delineation T Trigano, S Talala, D Luengo IEEE Journal of Biomedical and Health Informatics 27 (12), 5755-5766 , 2023 2023 Citations: 6
Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators T Trigano Smart Computing and Communication: 7th International Conference, SmartCom … , 2023 2023
Fast algorithm for time decay estimation with applications to electrostatic ion beam traps T Trigano, Z Fradkin Measurement Science and Technology 34 (2), 025701 , 2023 2023
Intracardiac ECG pulse localization using overlapping block sparse reconstruction T Trigano, D Luengo Biomedical Signal Processing and Control 79, 103921 , 2023 2023 Citations: 3
Parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators Z Chen, X Kong, X Zheng, Y Zhu, T Trigano International Conference on Smart Computing and Communication, 258-267 , 2022 2022 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Sparse Regression Algorithm for Activity Estimation in Spectrometry Y Sepulcre, T Trigano, Y Ritov IEEE Transactions on Signal Processing 61 (17), 4347-4359 , 2013 2013 Citations: 31
Statistical pileup correction method for HPGe detectors T Trigano, A Souloumiac, T Montagu, F Roueff, E Moulines IEEE Transactions on Signal Processing 55 (10), 4871-4881 , 2007 2007 Citations: 30
Semiparametric curve alignment and shift density estimation for biological data T Trigano, U Isserles, Y Ritov IEEE Transactions on Signal Processing 59 (5), 1970-1984 , 2011 2011 Citations: 28
Sparse spectral analysis of atrial fibrillation electrograms S Monzón, T Trigano, D Luengo, A Artes-Rodriguez 2012 IEEE International Workshop on Machine Learning for Signal Processing, 1-6 , 2012 2012 Citations: 26
Pileup correction algorithm using an iterated sparse reconstruction method T Trigano, I Gildin, Y Sepulcre IEEE Signal Processing Letters 22 (9), 1392-1395 , 2015 2015 Citations: 22
Cross-products LASSO D Luengo, J Vía, S Monzón, T Trigano, A Artés-Rodríguez 2013 IEEE International Conference on Acoustics, Speech and Signal … , 2013 2013 Citations: 21
Pile-up correction algorithms for nuclear spectrometry T Trigano, T Dautremer, E Barat, A Souloumiac Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech … , 2005 2005 Citations: 21
Fast digital filtering of spectrometric data for pile-up correction T Trigano, E Barat, T Dautremer, T Montagu IEEE Signal Processing Letters 22 (7), 973-977 , 2014 2014 Citations: 19
Blind analysis of atrial fibrillation electrograms: a sparsity-aware formulation D Luengo, S Monzón, T Trigano, J Vía, A Artés-Rodríguez Integrated Computer-Aided Engineering 22 (1), 71-85 , 2015 2015 Citations: 17
CoSA: An accelerated ISTA algorithm for dictionaries based on translated waveforms T Trigano, I Shevtsov, D Luengo Signal Processing 139, 131-135 , 2017 2017 Citations: 16
Traitement statistique du signal spectrométrique: étude du désempilement de spectre en énergie pour la spectrométrie Gamma T Trigano Télécom ParisTech , 2005 2005 Citations: 16
Nonparametric inference of photon energy distribution from indirect measurement É Moulines, F Roueff, A Souloumiac, T Trigano 2007 Citations: 15
An efficient method to learn overcomplete multi-scale dictionaries of ECG signals D Luengo, D Meltzer, T Trigano Applied Sciences 8 (12), 2569 , 2018 2018 Citations: 13
Sparse reconstruction algorithm for nonhomogeneous counting rate estimation T Trigano, Y Sepulcre, Y Ritov IEEE Transactions on Signal Processing 65 (2), 372-385 , 2016 2016 Citations: 13
On nonhomogeneous activity estimation in gamma spectrometry using sparse signal representation T Trigano, Y Sepulcre, M Roitman, U Aferiat 2011 IEEE Statistical Signal Processing Workshop (SSP), 649-652 , 2011 2011 Citations: 13
Grouped sparsity algorithm for multichannel intracardiac ECG synchronization T Trigano, V Kolesnikov, D Luengo, A Artés-Rodríguez 2014 22nd European Signal Processing Conference (EUSIPCO), 1537-1541 , 2014 2014 Citations: 12
Intensity estimation of spectroscopic signals with an improved sparse reconstruction algorithm T Trigano, J Cohen IEEE Signal Processing Letters 24 (5), 530-534 , 2017 2017 Citations: 10
Sparse ECG representation with a multi-scale dictionary derived from real-world signals D Luengo, D Meltzer, T Trigano 2018 41st International Conference on Telecommunications and Signal … , 2018 2018 Citations: 9
Process to isolate object of interest in image L SCHWARTZ, T Trigano, Y Bechor US Patent 10,417,772 , 2019 2019 Citations: 8
Pileup attenuation for spectroscopic signals using a sparse reconstruction M Lopatin, N Moskovitch, T Trigano, Y Sepulcre 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, 1-5 , 2012 2012 Citations: 8
