Zhaoxian Wu - RankMe

25 papers A* 1Misc 5Journal 19

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training. CoRR Quan Xiao, Jindan Li, Zhaoxian Wu, Tayfun Gokmen, Tianyi Chen
2025	J	`jnl`	Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions. CoRR Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Omobayode Fagbohungbe, Tianyi Chen
2025	J	`jnl`	In-memory Training on Analog Devices with Limited Conductance States via Multi-tile Residual Learning. CoRR Jindan Li, Zhaoxian Wu, Gaowen Liu, Tayfun Gokmen, Tianyi Chen
2025	J	`jnl`	On the Trade-Off Between Flatness and Optimization in Distributed Learning. IEEE Trans. Pattern Anal. Mach. Intell. Ying Cao, Zhaoxian Wu, Kun Yuan, Ali H. Sayed
2025	J	`jnl`	Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications. IEEE Trans. Signal Process. Yue Huang, Zhaoxian Wu, Shiqian Ma, Qing Ling
2024	J	`jnl`	Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment. IEEE Trans. Signal Process. Xingrong Dong, Zhaoxian Wu, Qing Ling, Zhi Tian
2024	Misc	`conf`	On the Convergence of Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness. ICASSP Yue Huang, Zhaoxian Wu, Qing Ling
2024	J	`jnl`	On the Trade-off between Flatness and Optimization in Distributed Learning. CoRR Ying Cao, Zhaoxian Wu, Kun Yuan, Ali H. Sayed
2024	J	`jnl`	Pipeline Gradient-based Model Training on Analog In-memory Accelerators. CoRR Zhaoxian Wu, Quan Xiao, Tayfun Gokmen, Hsinyu Tsai, Kaoutar El Maghraoui, Tianyi Chen
2024	J	`jnl`	Single-Timescale Multi-Sequence Stochastic Approximation Without Fixed Point Smoothness: Theories and Applications. CoRR Yue Huang, Zhaoxian Wu, Shiqian Ma, Qing Ling
2024	A*	`conf`	Towards Exact Gradient-based Training on Analog In-memory Computing. NeurIPS Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen
2024	J	`jnl`	Towards Exact Gradient-based Training on Analog In-memory Computing. CoRR Zhaoxian Wu, Tayfun Gokmen, Malte J. Rasch, Tianyi Chen
2023	J	`jnl`	Byzantine-Resilient Decentralized Stochastic Optimization With Robust Aggregation Rules. IEEE Trans. Signal Process. Zhaoxian Wu, Tianyi Chen, Qing Ling
2023	J	`jnl`	Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment. CoRR Xingrong Dong, Zhaoxian Wu, Qing Ling, Zhi Tian
2023	Misc	`conf`	Distributed Online Learning With Adversarial Participants In An Adversarial Environment. ICASSP Xingrong Dong, Zhaoxian Wu, Qing Ling, Zhi Tian
2022	Misc	`conf`	A Byzantine-Resilient Dual Subgradient Method for Vertical Federated Learning. ICASSP Kun Yuan, Zhaoxian Wu, Qing Ling
2022	J	`jnl`	Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rules. CoRR Zhaoxian Wu, Tianyi Chen, Qing Ling
2022	J	`jnl`	Byzantine-robust variance-reduced federated learning over distributed non-i.i.d. data. Inf. Sci. Jie Peng, Zhaoxian Wu, Qing Ling, Tianyi Chen
2022	J	`jnl`	Communication-Censored Distributed Stochastic Gradient Descent. IEEE Trans. Neural Networks Learn. Syst. Weiyu Li, Zhaoxian Wu, Tianyi Chen, Liping Li, Qing Ling
2021	J	`jnl`	Byzantine-Resilient Decentralized Policy Evaluation With Linear Function Approximation. IEEE Trans. Signal Process. Zhaoxian Wu, Han Shen, Tianyi Chen, Qing Ling
2021	Misc	`conf`	Byzantine-Resilient Decentralized TD Learning with Linear Function Approximation. ICASSP Zhaoxian Wu, Han Shen, Tianyi Chen, Qing Ling
2020	J	`jnl`	Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. Data. CoRR Jie Peng, Zhaoxian Wu, Qing Ling
2020	J	`jnl`	Federated Variance-Reduced Stochastic Gradient Descent With Robustness to Byzantine Attacks. IEEE Trans. Signal Process. Zhaoxian Wu, Qing Ling, Tianyi Chen, Georgios B. Giannakis
2020	Misc	`conf`	Resilient to Byzantine Attacks Finite-Sum Optimization Over Networks. ICASSP Zhaoxian Wu, Qing Ling, Tianyi Chen, Georgios B. Giannakis
2019	J	`jnl`	Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks. CoRR Zhaoxian Wu, Qing Ling, Tianyi Chen, Georgios B. Giannakis

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.