Benben Liu - RankMe

15 papers A* 1C 1Journal 10Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	DeepEBC: Compressing the Pre-Trained LLMs with Error-Bounded Lossy Compression. SCA/HPC Asia Workshops Jiaqi Xu, Zhaorui Zhang, Gaolin Wei, Sheng Di, Benben Liu, Xiaodong Yu, Xiaoyi Lu
2025	C	`conf`	A Dynamic Virtual Memory Management System for LLMs on AI Chips. ICCD Gaolin Wei, Zhaorui Zhang, Jiaqi Xu, Chen Jason Zhang, Xin Yao, Benben Liu
2025	J	`jnl`	CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning. CoRR Ping Zhang, Zhaorui Zhang, Sheng Di, Yao Xin, Benben Liu
2025	—	`conf`	Compression Error Sensitivity Analysis for Different Experts in MoE Model Inference. SC Workshops Songkai Ma, Zhaorui Zhang, Sheng Di, Benben Liu, Xiaodong Yu, Xiaoyi Lu, Dan Wang
2025	J	`jnl`	FedCSpc: A Cross-Silo Federated Learning System With Error-Bounded Lossy Parameter Compression. IEEE Trans. Parallel Distributed Syst. Zhaorui Zhang, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Zhuoran Ji, Benben Liu, Khalid Ayedh Alharthi, Jiannong Cao, Franck Cappello
2025	J	`jnl`	FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression. IEEE Trans. Parallel Distributed Syst. Zhaorui Zhang, Sheng Di, Benben Liu, Zhuoran Ji, Guanpeng Li, Xiaoyi Lu, Amelie Chi Zhou, Khalid Ayedh Alharthi, Jiannong Cao
2025	J	`jnl`	HLoRA: Efficient Federated Learning System for LLM Heterogeneous Fine-Tuning. CoRR Qianli Liu, Zhaorui Zhang, Xin Yao, Benben Liu
2025	J	`jnl`	MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model? CoRR Songkai Ma, Zhaorui Zhang, Sheng Di, Benben Liu, Xiaodong Yu, Xiaoyi Lu, Dan Wang
2024	A*	`conf`	FedFa: A Fully Asynchronous Training Paradigm for Federated Learning. IJCAI Haotian Xu, Zhaorui Zhang, Sheng Di, Benben Liu, Khalid Ayedh Alharthi, Jiannong Cao
2024	J	`jnl`	FedFa: A Fully Asynchronous Training Paradigm for Federated Learning. CoRR Haotian Xu, Zhaorui Zhang, Sheng Di, Benben Liu, Khalid Ayedh Alharthi, Jiannong Cao
2016	J	`jnl`	FPGA-Based High-Performance Collision Detection: An Enabling Technique for Image-Guided Robotic Surgery. Frontiers Robotics AI Zhaorui Zhang, Yao Xin, Benben Liu, Will X. Y. Li, Kit-Hang Lee, Chun-Fai Ng, Danail Stoyanov, Ray C. C. Cheung, Ka-Wai Kwok
2014	J	`jnl`	Design Exploration of Geometric Biclustering for Microarray Data Analysis in Data Mining. IEEE Trans. Parallel Distributed Syst. Benben Liu, Chi Wai Yu, Doris Z. Wang, Ray C. C. Cheung, Hong Yan
2014	J	`jnl`	GPU-based biclustering for microarray data analysis in neurocomputing. Neurocomputing Benben Liu, Yao Xin, Ray C. C. Cheung, Hong Yan
2013	J	`jnl`	Parallel architecture for DNA sequence inexact matching with Burrows-Wheeler Transform. Microelectron. J. Yao Xin, Benben Liu, Biao Min, Will X. Y. Li, Ray C. C. Cheung, Anthony S. Fong, Ting-Fung Chan
2012	—	`conf`	GPU-Based Biclustering for Neural Information Processing. ICONIP (5) Alan W. Y. Lo, Benben Liu, Ray C. C. Cheung

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.