Qinqin Zhu

15 papers A 1C 3Journal 9Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	DCFICSH: A Dual-Channel Fusion Model Combining Multi-Modal Data for Identifying Cell-Specific Silencers and Their Strength in the Human Genome. ICIC (19) Jingdong Yuan, Qinqin Zhu, Haolu Zhou, Yu Han, Yun Zuo, Yude Bai, Wenying He
2025	—	`conf`	Multi-Hop and Multi-Path Computation Offloading for Integrated Access and Backhaul Networks Assisted by Multi-relay UAV. CNIOT Zichao Gong, Ronggui Zou, Xiaoyang Zhou, Rongrong Xu, Qinqin Zhu, Yan Bao
2024	A	`conf`	Ads Supply Personalization via Doubly Robust Learning. CIKM Wei Shi, Chen Fu, Qi Xu, Sanjian Chen, Jizhe Zhang, Qinqin Zhu, Zhigang Hua, Shuang Yang
2024	J	`jnl`	Ads Supply Personalization via Doubly Robust Learning. CoRR Wei Shi, Chen Fu, Qi Xu, Sanjian Chen, Jizhe Zhang, Qinqin Zhu, Zhigang Hua, Shuang Yang
2024	J	`jnl`	An Efficient Dynamic Auto-Regressive CCA for Time Series Imputation With Irregular Sampling. IEEE Trans Autom. Sci. Eng. Bo Xu, Qinqin Zhu
2023	C	`conf`	Dynamic Probabilistic Latent Variable Model with Exogenous Variables for Dynamic Anomaly Detection. ACC Bo Xu, Qinqin Zhu
2022	J	`jnl`	Dynamic Probabilistic Predictable Feature Analysis for Multivariate Temporal Process Monitoring. IEEE Trans. Control. Syst. Technol. Wei Fan, Qinqin Zhu, Shaojun Ren, Liang Zhang, Fengqi Si
2022	J	`jnl`	Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network. Neurocomputing Nima Amini, Qinqin Zhu
2022	J	`jnl`	Improved manifold sparse slow feature analysis for process monitoring. Comput. Chem. Eng. Hussein Saafan, Qinqin Zhu
2021	J	`jnl`	A Novel Multi-Mode Bayesian Method for the Process Monitoring and Fault Diagnosis of Coal Mills. IEEE Access Wei Fan, Shaojun Ren, Qinqin Zhu, Zhijun Jia, Delong Bai, Fengqi Si
2021	C	`conf`	Comprehensive Monitoring with Incremental Slow Feature Analysis. ACC Hussein Saafan, Qinqin Zhu
2020	J	`jnl`	Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring. Annu. Rev. Control. Si-Zhao Joe Qin, Yining Dong, Qinqin Zhu, Jin Wang, Qiang Liu
2020	J	`jnl`	Dynamic latent variable regression for inferential sensor modeling and monitoring. Comput. Chem. Eng. Qinqin Zhu, Si-Zhao Joe Qin, Yining Dong
2020	J	`jnl`	Latent variable regression for supervised modeling and monitoring. IEEE CAA J. Autom. Sinica Qinqin Zhu
2017	C	`conf`	Quality-relevant fault detection of nonlinear processes based on kernel concurrent canonical correlation analysis. ACC Qinqin Zhu, Qiang Liu, Si-Zhao Joe Qin

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.