Qi Zhang - RankMe

18 papers A* 6A 1Journal 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Turning Semantics into Topology: LLM-Driven Attribute Augmentation for Collaborative Filtering. CoRR Junjie Meng, Ranxu Zhang, Wei Wu, Rui Zhang, Chuan Qin, Qi Zhang, Qi Liu, Hui Xiong, Chao Wang
2025	J	`jnl`	A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics. Proc. IEEE Chuan Qin, Le Zhang, Yihang Cheng, Rui Zha, Dazhong Shen, Qi Zhang, Xi Chen, Ying Sun, Chen Zhu, Hengshu Zhu, Hui Xiong
2025	J	`jnl`	ChemATP: A Training-Free Chemical Reasoning Framework for Large Language Models. CoRR Mingxu Zhang, Dazhong Shen, Qi Zhang, Ying Sun
2025	J	`jnl`	Handling Over-Smoothing and Over-Squashing in Graph Convolution With Maximization Operation. IEEE Trans. Neural Networks Learn. Syst. Dazhong Shen, Chuan Qin, Qi Zhang, Hengshu Zhu, Hui Xiong
2025	A	`conf`	I-Lora: Iterative Merging of Routing-Tuned Low-Rank Adapters for Multi-Task Learning. ICME Guoqing Zhao, Qi Zhang, Shaopeng Zhai, Dazhong Shen, Tianyi Zhang, Yu Qiao, Tong Xu
2024	J	`jnl`	CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation. CoRR Fuxian Huang, Qi Zhang, Shaopeng Zhai, Jie Wang, Tianyi Zhang, Haoran Zhang, Ming Zhou, Yu Liu, Yu Qiao
2024	J	`jnl`	Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting. CoRR Zhuoning Guo, Hao Liu, Le Zhang, Qi Zhang, Hengshu Zhu, Hui Xiong
2024	A*	`conf`	DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation. ICDM Xiaoshan Yu, Chuan Qin, Qi Zhang, Chen Zhu, Haiping Ma, Xingyi Zhang, Hengshu Zhu
2024	J	`jnl`	DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation. CoRR Xiaoshan Yu, Chuan Qin, Qi Zhang, Chen Zhu, Haiping Ma, Xingyi Zhang, Hengshu Zhu
2024	A*	`conf`	Graph Signal Diffusion Model for Collaborative Filtering. SIGIR Yunqin Zhu, Chao Wang, Qi Zhang, Hui Xiong
2023	J	`jnl`	A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics. CoRR Chuan Qin, Le Zhang, Rui Zha, Dazhong Shen, Qi Zhang, Ying Sun, Chen Zhu, Hengshu Zhu, Hui Xiong
2023	J	`jnl`	Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern Analysis. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Qi Zhang, Hengshu Zhu, Peng Wang, Enhong Chen, Hui Xiong
2023	A*	`conf`	RecruitPro: A Pretrained Language Model with Skill-Aware Prompt Learning for Intelligent Recruitment. KDD Chuyu Fang, Chuan Qin, Qi Zhang, Kaichun Yao, Jingshuai Zhang, Hengshu Zhu, Fuzhen Zhuang, Hui Xiong
2022	A*	`conf`	Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning. KDD Zhuoning Guo, Hao Liu, Le Zhang, Qi Zhang, Hengshu Zhu, Hui Xiong
2021	J	`jnl`	Exploiting Real-time Search Engine Queries for Earthquake Detection: A Summary of Results. ACM Trans. Inf. Syst. Qi Zhang, Hengshu Zhu, Qi Liu, Enhong Chen, Hui Xiong
2021	A*	`conf`	Talent Demand Forecasting with Attentive Neural Sequential Model. KDD Qi Zhang, Hengshu Zhu, Ying Sun, Hao Liu, Fuzhen Zhuang, Hui Xiong
2019	A*	`conf`	Aftershock Detection with Multi-scale Description Based Neural Network. ICDM Qi Zhang, Tong Xu, Hengshu Zhu, Lifu Zhang, Hui Xiong, Enhong Chen, Qi Liu
2019	J	`jnl`	Machine Learning-enhanced Realistic Framework for Real-time Seismic Monitoring - The Winning Solution of the 2017 International Aftershock Detection Contest. CoRR Dazhong Shen, Qi Zhang, Tong Xu, Hengshu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, Lihua Fang, Enhong Chen, Hui Xiong

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.