Baptiste Lafabregue

18 papers B 1C 3Journal 6Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Visualization for Comparative Analysis of Regression Models. CoRR Nassime Mountasir, Baptiste Lafabregue, Bruno Albert, Nicolas Lachiche
2025	—	`conf`	Apprendre avec peu d'exemples : Une approche auto-supervisée basée sur les segments avec application à la télédétection. EGC Antoine Saget, Baptiste Lafabregue, Antoine Cornuéjols, Pierre Gançarski
2025	C	`conf`	Dynamic Time Warping-Based Evidential Clustering of Time Series with c-Medoids. ICMLA Eya Laffet, Ahmed Samet, Mohamed Anis Bach Tobji, Baptiste Lafabregue, François de Bertrand de Beuvron
2025	—	`ed.`	Extraction et Gestion des Connaissances, EGC 2025, Strasbourg, France, January 29-31, 2025. Strasbourg, France Thomas Guyet, Baptiste Lafabregue, Aurélie Leborgne
2025	J	`jnl`	I-SAMARAH, an incremental constrained clustering applied to remote sensing images. Neural Comput. Appl. Baptiste Lafabregue, Pierre Gançarski
2025	—	`conf`	Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing. TerraBytes Antoine Saget, Baptiste Lafabregue, Antoine Cornuéjols, Pierre Gançarski
2025	J	`jnl`	Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing. CoRR Antoine Saget, Baptiste Lafabregue, Antoine Cornuéjols, Pierre Gançarski
2024	—	`conf`	Écrêter la valeur cible ou filtrer les données en maintenance prévisionnelle : exemple de C-MAPSS. EGC Nassime Mountasir, Baptiste Lafabregue, Bruno Albert, Nicolas Lachiche
2022	C	`conf`	Deep Clustering Methods Study Applied to Satellite Images Time Series. IGARSS Baptiste Lafabregue, Anne Puissant, Jonathan Weber, Germain Forestier
2022	J	`jnl`	End-to-end deep representation learning for time series clustering: a comparative study. Data Min. Knowl. Discov. Baptiste Lafabregue, Jonathan Weber, Pierre Gançarski, Germain Forestier
2022	—	`conf`	Incremental constrained clustering with application to remote sensing images time series. ICDM (Workshops) Baptiste Lafabregue, Pierre Gançarski, Jonathan Weber, Germain Forestier
2021	—	`—`	Clustering et apprentissage profond sous contraintes pour l'analyse de séries temporelles: application à l'analyse temporelle incrémentale en télédétection. (Constrained clustering and deep learning for time series analysis: with application to incremental temporal analysis for remote sensing). Baptiste Lafabregue
2021	B	`conf`	Grad Centroid Activation Mapping for Convolutional Neural Networks. ICTAI Baptiste Lafabregue, Jonathan Weber, Pierre Gançarski, Germain Forestier
2020	—	`conf`	FODOMUST - Une plateforme de clustering collaboratif sous contraintes incrémental de séries temporelles. EGC Pierre Gançarski, Baptiste Lafabregue, Abdoul-Djawadou Salaou, Harrison Vernier
2019	C	`conf`	Constrained Distance based K-Means Clustering for Satellite Image Time-Series. IGARSS Thomas Andrew Lampert, Baptiste Lafabregue, Pierre Gançarski
2019	J	`jnl`	Constrained Distance-Based Clustering for Satellite Image Time-Series. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. Thomas Andrew Lampert, Baptiste Lafabregue, Thi-Bich-Hanh Dao, Nicolas Serrette, Christel Vrain, Pierre Gançarski
2019	—	`conf`	Deep Constrained Clustering applied to Satellite Image Time Series. MACLEAN@PKDD/ECML Baptiste Lafabregue, Jonathan Weber, Pierre Gançarski, Germain Forestier
2018	J	`jnl`	Constrained distance based clustering for time-series: a comparative and experimental study. Data Min. Knowl. Discov. Thomas Andrew Lampert, Thi-Bich-Hanh Dao, Baptiste Lafabregue, Nicolas Serrette, Germain Forestier, Bruno Crémilleux, Christel Vrain, Pierre Gançarski

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.