Barbara Pascal

23 papers B 1Misc 2Journal 11Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Risk estimate under a time-varying autoregressive model for data-driven reproduction number estimation. Signal Process. Barbara Pascal, Samuel Vaiter
2025	—	`conf`	Detectability of patches in fractal textures for assessing Hölder exponent-based breast cancer risk evaluation. SSP Barbara Pascal, Hermine Biermé
2025	Misc	`conf`	Hierarchical Bayesian Estimation of COVID-19 Reproduction Number. ICASSP Patrice Abry, Juliette Chevallier, Gersende Fort, Barbara Pascal
2025	—	`conf`	Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems. SSP Bernardin Tamo Amougou, Marcelo Pereyra, Barbara Pascal
2025	J	`jnl`	Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems. CoRR Bernardin Tamo Amougou, Marcelo Pereyra, Barbara Pascal
2024	—	`conf`	On the Robustness of Musical Timbre Perception Models: From Perceptual to Learned Approaches. EUSIPCO Barbara Pascal, Mathieu Lagrange
2024	J	`jnl`	Point Processes and spatial statistics in time-frequency analysis. CoRR Barbara Pascal, Rémi Bardenet
2024	—	`conf`	Synthetic Spatiotemporal Covid19 Infection Counts to Assess Graph-Regularized Estimation of Multivariate Reproduction Numbers. EUSIPCO Juliana Du, Barbara Pascal, Patrice Abry
2023	J	`jnl`	Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers. IEEE Trans. Signal Process. Gersende Fort, Barbara Pascal, Patrice Abry, Nelly Pustelnik
2023	J	`jnl`	Hyperparameter selection for Discrete Mumford-Shah. Signal Image Video Process. Charles-Gérard Lucas, Barbara Pascal, Nelly Pustelnik, Patrice Abry
2023	—	`conf`	Pandemic Intensity Estimation from Stochastic Approximation-Based Algorithms. CAMSAP Patrice Abry, Juliette Chevallier, Gersende Fort, Barbara Pascal
2023	—	`conf`	Proximal-Langevin Samplers for Nonsmooth Composite Posteriors: Application to the Estimation of Covid19 Reproduction Number. EUSIPCO Patrice Abry, Gersende Fort, Barbara Pascal, Nelly Pustelnik
2022	J	`jnl`	A Covariant, Discrete Time-Frequency Representation Tailored for Zero-Based Signal Detection. IEEE Trans. Signal Process. Barbara Pascal, Rémi Bardenet
2022	J	`jnl`	Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers. CoRR Gersende Fort, Barbara Pascal, Patrice Abry, Nelly Pustelnik
2022	—	`conf`	Credibility interval Design for Covid19 Reproduction Number from Nonsmooth Langevin-type Monte Carlo sampling. EUSIPCO Hugo Artigas, Barbara Pascal, Gersende Fort, Patrice Abry, Nelly Pustelnik
2022	J	`jnl`	Nonsmooth Convex Optimization to Estimate the Covid-19 Reproduction Number Space-Time Evolution With Robustness Against Low Quality Data. IEEE Trans. Signal Process. Barbara Pascal, Patrice Abry, Nelly Pustelnik, Stéphane G. Roux, Rémi Gribonval, Patrick Flandrin
2022	—	`conf`	Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling. EMBC Patrice Abry, Gersende Fort, Barbara Pascal, Nelly Pustelnik
2021	J	`jnl`	Automated Data-Driven Selection of the Hyperparameters for Total-Variation-Based Texture Segmentation. J. Math. Imaging Vis. Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry
2020	J	`jnl`	Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. CoRR Barbara Pascal, Samuel Vaiter, Nelly Pustelnik, Patrice Abry
2020	J	`jnl`	Parameter-free and fast nonlinear piecewise filtering: application to experimental physics. Ann. des Télécommunications Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Géminard, Valérie Vidal
2020	—	`conf`	Scale-free Texture Segmentation: Expert Feature-based versus Deep Learning strategies. EUSIPCO Barbara Pascal, Vincent Mauduit, Nelly Pustelnik, Patrice Abry
2018	Misc	`conf`	Block-Coordinate Proximal Algorithms for Scale-Free Texture Segmentation. ICASSP Barbara Pascal, Nelly Pustelnik, Patrice Abry, Jean-Christophe Pesquet
2018	B	`conf`	Joint Estimation of Local Variance and Local Regularity for Texture Segmentation. Application to Multiphase Flow Characterization. ICIP Barbara Pascal, Nelly Pustelnik, Patrice Abry, Marion Serres, Valérie Vidal

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.