Fatemeh Nassajian Mojarrad

11 papers Journal 10Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning a robust shape parameter for RBF approximation. Appl. Math. Comput. Maria Han Veiga, Faezeh Nassajian Mojarrad, Fatemeh Nassajian Mojarrad
2024	—	`conf`	FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking. CHIL Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet
2024	J	`jnl`	FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking. CoRR Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet
2024	J	`jnl`	HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph Attention. CoRR Lorenzo Bini, Fatemeh Nassajian Mojarrad, Thomas Matthes, Stéphane Marchand-Maillet
2024	J	`jnl`	Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction. CoRR Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet
2024	J	`jnl`	Learning a robust shape parameter for RBF approximation. CoRR Maria Han Veiga, Fatemeh Nassajian Mojarrad
2024	J	`jnl`	Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph. CoRR Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet
2023	J	`jnl`	A new variable shape parameter strategy for RBF approximation using neural networks. Comput. Math. Appl. Fatemeh Nassajian Mojarrad, Maria Han Veiga, Jan S. Hesthaven, Philipp Öffner
2023	J	`jnl`	An arbitrarily high order and asymptotic preserving kinetic scheme in compressible fluid dynamic. CoRR Rémi Abgrall, Fatemeh Nassajian Mojarrad
2022	J	`jnl`	A new variable shape parameter strategy for RBF approximation using neural networks. CoRR Fatemeh Nassajian Mojarrad, Maria Han Veiga, Jan S. Hesthaven, Philipp Öffner
2022	J	`jnl`	Conservative scheme compatible with some other conservation laws: conservation of the local angular momentum. CoRR Rémi Abgrall, Fatemeh Nassajian Mojarrad

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.