Sami Jullien - RankMe

15 papers A* 5A 2Journal 8

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	Distributional Reinforcement Learning with Dual Expectile-Quantile Regression. UAI Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth, Maarten de Rijke
2024	A*	`conf`	Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation? SIGIR Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Andrew Yates, Mohammad Aliannejadi, Maarten de Rijke
2024	J	`jnl`	Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation? CoRR Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Mohammad Aliannejadi, Andrew Yates, Maarten de Rijke
2023	J	`jnl`	A Next Basket Recommendation Reality Check. ACM Trans. Inf. Syst. Ming Li, Sami Jullien, Mozhdeh Ariannezhad, Maarten de Rijke
2023	J	`jnl`	A Simulation Environment and Reinforcement Learning Method for Waste Reduction. Trans. Mach. Learn. Res. Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke
2023	A*	`conf`	Complex Item Set Recommendation. SIGIR Mozhdeh Ariannezhad, Ming Li, Sami Jullien, Maarten de Rijke
2023	J	`jnl`	Distributional Reinforcement Learning with Dual Expectile-Quantile Regression. CoRR Sami Jullien, Romain Deffayet, Jean-Michel Renders, Paul Groth, Maarten de Rijke
2023	J	`jnl`	Report on the 1st Workshop on Generative Information Retrieval (Gen-IR 2023) at SIGIR 2023. SIGIR Forum Gabriel Bénédict, Ruqing Zhang, Donald Metzler, Andrew Yates, Romain Deffayet, Philipp Hager, Sami Jullien
2022	J	`jnl`	GLDQN: Explicitly Parameterized Quantile Reinforcement Learning for Waste Reduction. CoRR Sami Jullien, Mozhdeh Ariannezhad, Paul Groth, Maarten de Rijke
2022	A*	`conf`	ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping. SIGIR Mozhdeh Ariannezhad, Sami Jullien, Ming Li, Min Fang, Sebastian Schelter, Maarten de Rijke
2022	A*	`conf`	Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence. AAAI Ana Lucic, Maurits J. R. Bleeker, Sami Jullien, Samarth Bhargav, Maarten de Rijke
2022	A*	`conf`	Towards Reproducible Machine Learning Research in Information Retrieval. SIGIR Ana Lucic, Maurits J. R. Bleeker, Maarten de Rijke, Koustuv Sinha, Sami Jullien, Robert Stojnic
2021	J	`jnl`	A Next Basket Recommendation Reality Check. CoRR Ming Li, Sami Jullien, Mozhdeh Ariannezhad, Maarten de Rijke
2021	J	`jnl`	Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence through the Lens of Reproducibility. CoRR Ana Lucic, Maurits J. R. Bleeker, Sami Jullien, Samarth Bhargav, Maarten de Rijke
2021	A	`conf`	Understanding Multi-channel Customer Behavior in Retail. CIKM Mozhdeh Ariannezhad, Sami Jullien, Pim Nauts, Min Fang, Sebastian Schelter, Maarten de Rijke

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.