Abdullah Alchihabi

24 papers A* 1A 4C 1Journal 14Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	A Unified Framework for Heterogeneous Semi-supervised Learning. CVPR Marzi Heidari, Abdullah Alchihabi, Hao Yan, Yuhong Guo
2025	J	`jnl`	A Unified Framework for Heterogeneous Semi-supervised Learning. CoRR Marzi Heidari, Abdullah Alchihabi, Hao Yan, Yuhong Guo
2025	J	`jnl`	Unbiased GNN Learning via Fairness-Aware Subgraph Diffusion. CoRR Abdullah Alchihabi, Yuhong Guo
2025	A	`conf`	Zero-Shot Action Generalization with Limited Observations. AISTATS Abdullah Alchihabi, Hanping Zhang, Yuhong Guo
2025	J	`jnl`	Zero-Shot Action Generalization with Limited Observations. CoRR Abdullah Alchihabi, Hanping Zhang, Yuhong Guo
2024	A	`conf`	Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification. AISTATS Marzi Heidari, Abdullah Alchihabi, Qing En, Yuhong Guo
2024	A	`conf`	Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning. BMVC Abdullah Alchihabi, Marzi Heidari, Yuhong Guo
2024	J	`jnl`	Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations. CoRR Abdullah Alchihabi, Hao Yan, Yuhong Guo
2023	J	`jnl`	Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification. CoRR Marzi Heidari, Abdullah Alchihabi, Qing En, Yuhong Guo
2023	J	`jnl`	Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning. CoRR Abdullah Alchihabi, Marzi Heidari, Yuhong Guo
2023	—	`conf`	Efficient Low-Rank GNN Defense Against Structural Attacks. ICKG Abdullah Alchihabi, Qing En, Yuhong Guo
2023	J	`jnl`	Efficient Low-Rank GNN Defense Against Structural Attacks. CoRR Abdullah Alchihabi, Qing En, Yuhong Guo
2023	—	`conf`	GDM: Dual Mixup for Graph Classification with Limited Supervision. ECML/PKDD (3) Abdullah Alchihabi, Yuhong Guo
2023	J	`jnl`	GDM: Dual Mixup for Graph Classification with Limited Supervision. CoRR Abdullah Alchihabi, Yuhong Guo
2023	A	`conf`	Learning Robust Graph Neural Networks with Limited Supervision. AISTATS Abdullah Alchihabi, Yuhong Guo
2021	J	`jnl`	Analyzing Complex Problem Solving by Dynamic Brain Networks. Frontiers Neuroinformatics Abdullah Alchihabi, Omer Ekmekci, Baran Baris Kivilcim, Sharlene D. Newman, Fatos T. Yarman-Vural
2021	J	`jnl`	Dual GNNs: Graph Neural Network Learning with Limited Supervision. CoRR Abdullah Alchihabi, Yuhong Guo
2019	J	`jnl`	A generic framework for optimizing performance metrics by tuning parameters of clustering protocols in WSNs. Wirel. Networks Abdullah Alchihabi, Ates Dervis, Enver Ever, Fadi Al-Turjman
2018	J	`jnl`	A Two-Tier Distributed Fuzzy Logic Based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks. IEEE Trans. Fuzzy Syst. Seyyit Alper Sert, Abdullah Alchihabi, Adnan Yazici
2018	—	`conf`	A dynamic network representation of fMRI for modeling and analyzing the problem solving task. ISBI Abdullah Alchihabi, Baran B. Kivilicim, Sharlene D. Newman, Fatos T. Yarman-Vural
2018	—	`conf`	Decoding cognitive subtasks of complex problem solving using fMRI signals. SIU Abdullah Alchihabi, Baran B. Kivilicim, Omer Ekmekci, Sharlene D. Newman, Fatos T. Yarman-Vural
2018	J	`jnl`	On the Brain Networks of Complex Problem Solving. CoRR Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilicim, Sharlene D. Newman, Fatos T. Yarman-Vural
2017	C	`conf`	Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning. BIBE Arash Rahnama, Abdullah Alchihabi, Vijay Gupta, Panos J. Antsaklis, Fatos T. Yarman-Vural
2017	J	`jnl`	Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning. CoRR Arash Rahnama, Abdullah Alchihabi, Vijay Gupta, Panos J. Antsaklis, Fatos T. Yarman-Vural

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.