Gang Yan

18 papers A* 7A 3B 1Journal 5Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	FedDiAL: Adaptive Federated Learning with Hierarchical Discriminative Network for Large Pre-trained Models. KDD (2) Gang Yan, Wan Du
2025	A	`conf`	FedSTEP: Asynchronous and Staleness-Aware Personalization for Efficient Federated Learning. CIKM Gang Yan, Jian Li, Wan Du
2024	A*	`conf`	DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations. AAAI Guojun Xiong, Gang Yan, Shiqiang Wang, Jian Li
2024	A	`conf`	Enhancing Model Poisoning Attacks to Byzantine-Robust Federated Learning via Critical Learning Periods. RAID Gang Yan, Hao Wang, Xu Yuan, Jian Li
2024	A*	`conf`	FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation. KDD Gang Yan, Hao Wang, Xu Yuan, Jian Li
2024	B	`conf`	Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates. MobiHoc Guojun Xiong, Gang Yan, Shiqiang Wang, Jian Li
2023	A*	`conf`	CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. KDD Gang Yan, Hao Wang, Xu Yuan, Jian Li
2023	A*	`conf`	DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness. AAAI Gang Yan, Hao Wang, Xu Yuan, Jian Li
2023	J	`jnl`	DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations. CoRR Guojun Xiong, Gang Yan, Shiqiang Wang, Jian Li
2023	J	`jnl`	Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach. IEEE/ACM Trans. Netw. Guojun Xiong, Shufan Wang, Gang Yan, Jian Li
2023	J	`jnl`	Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates. CoRR Guojun Xiong, Gang Yan, Shiqiang Wang, Jian Li
2022	A*	`conf`	Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach. INFOCOM Guojun Xiong, Shufan Wang, Gang Yan, Jian Li
2022	A*	`conf`	Seizing Critical Learning Periods in Federated Learning. AAAI Gang Yan, Hao Wang, Jian Li
2022	—	`conf`	Towards Latency Awareness for Content Delivery Network Caching. USENIX ATC Gang Yan, Jian Li
2021	J	`jnl`	Critical Learning Periods in Federated Learning. CoRR Gang Yan, Hao Wang, Jian Li
2021	A	`conf`	Learning from optimal caching for content delivery. CoNEXT Gang Yan, Jian Li, Don Towsley
2021	J	`jnl`	Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers. CoRR Guojun Xiong, Gang Yan, Rahul Singh, Jian Li
2020	A*	`conf`	RL-Bélády: A Unified Learning Framework for Content Caching. ACM Multimedia Gang Yan, Jian Li

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.