Adil Zouitine - RankMe

11 papers A* 2Journal 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	LeRobot: An Open-Source Library for End-to-End Robot Learning. CoRR Rémi Cadène, Simon Alibert, Francesco Capuano, Michel Aractingi, Adil Zouitine, Pepijn Kooijmans, Jade Choghari, Martino Russi, Caroline Pascal, Steven Palma, Mustafa Shukor, Jess Moss, Alexander Soare, Dana Aubakirova, Quentin Lhoest, Quentin Gallouédec, Thomas Wolf
2025	J	`jnl`	Robot Learning: A Tutorial. CoRR Francesco Capuano, Caroline Pascal, Adil Zouitine, Thomas Wolf, Michel Aractingi
2025	J	`jnl`	SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics. CoRR Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andrés Marafioti, Simon Alibert, Matthieu Cord, Thomas Wolf, Rémi Cadène
2024	J	`jnl`	RRLS : Robust Reinforcement Learning Suite. CoRR Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, Emmanuel Rachelson
2024	J	`jnl`	Solving robust MDPs as a sequence of static RL problems. CoRR Adil Zouitine, Matthieu Geist, Emmanuel Rachelson
2024	A*	`conf`	Time-Constrained Robust MDPs. NeurIPS Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, Emmanuel Rachelson
2024	J	`jnl`	Time-Constrained Robust MDPs. CoRR Adil Zouitine, David Bertoin, Pierre Clavier, Matthieu Geist, Emmanuel Rachelson
2022	A*	`conf`	Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning. NeurIPS David Bertoin, Adil Zouitine, Mehdi Zouitine, Emmanuel Rachelson
2022	J	`jnl`	Look where you look! Saliency-guided Q-networks for visual RL tasks. CoRR David Bertoin, Adil Zouitine, Mehdi Zouitine, Emmanuel Rachelson
2021	J	`jnl`	River: machine learning for streaming data in Python. J. Mach. Learn. Res. Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphaël Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet
2020	J	`jnl`	River: machine learning for streaming data in Python. CoRR Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphaël Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet

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.