Zhangdie Yuan - RankMe

14 papers A* 1A 1Journal 12

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Assessing Large Language Models in Updating Their Forecasts with New Information. CoRR Zhangdie Yuan, Zifeng Ding, Andreas Vlachos
2025	J	`jnl`	Capturing Symmetry and Antisymmetry in Language Models through Symmetry-Aware Training Objectives. CoRR Zhangdie Yuan, Andreas Vlachos
2025	J	`jnl`	Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning. CoRR Zifeng Ding, Shenyang Huang, Zeyu Cao, Emma Kondrup, Zachary Yang, Xingyue Huang, Yuan Sui, Zhangdie Yuan, Yuqicheng Zhu, Xianglong Hu, Yuan He, Farimah Poursafaei, Michael M. Bronstein, Andreas Vlachos
2025	J	`jnl`	TCP: a Benchmark for Temporal Constraint-Based Planning. CoRR Zifeng Ding, Sikuan Yan, Zhangdie Yuan, Xianglong Hu, Fangru Lin, Andreas Vlachos
2025	J	`jnl`	The Future Outcome Reasoning and Confidence Assessment Benchmark. CoRR Zhangdie Yuan, Zifeng Ding, Andreas Vlachos
2025	J	`jnl`	Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation. CoRR Zhangdie Yuan, Han-Chin Shing, Mitch Strong, Chaitanya Shivade
2024	J	`jnl`	DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models. CoRR Songbo Hu, Xiaobin Wang, Zhangdie Yuan, Anna Korhonen, Ivan Vulic
2024	J	`jnl`	Language Models as Hierarchy Encoders. CoRR Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks
2024	J	`jnl`	PRobELM: Plausibility Ranking Evaluation for Language Models. CoRR Zhangdie Yuan, Chenxi Whitehouse, Eric Chamoun, Rami Aly, Andreas Vlachos
2023	A	`conf`	Can Pretrained Language Models (Yet) Reason Deductively? EACL Zhangdie Yuan, Songbo Hu, Ivan Vulic, Anna Korhonen, Zaiqiao Meng
2023	J	`jnl`	Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs. CoRR Zhangdie Yuan, Andreas Vlachos
2022	J	`jnl`	Can Pretrained Language Models (Yet) Reason Deductively? CoRR Zhangdie Yuan, Songbo Hu, Ivan Vulic, Anna Korhonen, Zaiqiao Meng
2022	A*	`conf`	Varifocal Question Generation for Fact-checking. EMNLP Nedjma Ousidhoum, Zhangdie Yuan, Andreas Vlachos
2022	J	`jnl`	Varifocal Question Generation for Fact-checking. CoRR Nedjma Ousidhoum, Zhangdie Yuan, Andreas Vlachos

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.