A. Ian McLeod

11 papers A* 2Journal 9

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Uniform Noise Distribution and Compact Clusters: Unveiling the Success of Self-Supervised Learning in Label Noise. Trans. Mach. Learn. Res. Pengcheng Xu, Li Yi, Gezheng Xu, Xi Chen, A. Ian McLeod, Charles Ling, Boyu Wang
2025	J	`jnl`	Unraveling the Mysteries of Label Noise in Source-Free Domain Adaptation: Theory and Practice. IEEE Trans. Pattern Anal. Mach. Intell. Gezheng Xu, Li Yi, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Changjian Shui, A. Ian McLeod, Boyu Wang, Charles Ling
2023	A*	`conf`	When Source-Free Domain Adaptation Meets Learning with Noisy Labels. ICLR Li Yi, Gezheng Xu, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Charles Ling, A. Ian McLeod, Boyu Wang
2023	J	`jnl`	When Source-Free Domain Adaptation Meets Learning with Noisy Labels. CoRR Li Yi, Gezheng Xu, Pengcheng Xu, Jiaqi Li, Ruizhi Pu, Charles Ling, A. Ian McLeod, Boyu Wang
2022	A*	`conf`	On Learning Contrastive Representations for Learning with Noisy Labels. CVPR Li Yi, Sheng Liu, Qi She, A. Ian McLeod, Boyu Wang
2022	J	`jnl`	On Learning Contrastive Representations for Learning with Noisy Labels. CoRR Li Yi, Sheng Liu, Qi She, A. Ian McLeod, Boyu Wang
2013	J	`jnl`	Developments in Maximum Likelihood Unit Root Tests. Commun. Stat. Simul. Comput. Ying Zhang, Hao Yu, A. Ian McLeod
2008	J	`jnl`	Faster ARMA maximum likelihood estimation. Comput. Stat. Data Anal. A. Ian McLeod, Ying Zhang
2006	J	`jnl`	Improved Pena-Rodriguez portmanteau test. Comput. Stat. Data Anal. Jen-Wen Lin, A. Ian McLeod
2005	J	`jnl`	Power Computations for Intervention Analysis. Technometrics A. Ian McLeod, E. R. Vingilis
2001	J	`jnl`	Mean likelihood estimators. Stat. Comput. A. Ian McLeod, Benoit Quenneville

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.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

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.