Zahra Monfared

19 papers A* 8Journal 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures. CoRR Sucheta Ghosh, Zahra Monfared, Felix Dietrich
2026	J	`jnl`	Electrocardiogram Classification with Transformers Using Koopman and Wavelet Features. CoRR Sucheta Ghosh, Zahra Monfared
2026	J	`jnl`	Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis. CoRR Anima Kujur, Zahra Monfared
2025	J	`jnl`	Detecting Invariant Manifolds in ReLU-Based RNNs. CoRR Lukas Eisenmann, Alena Brändle, Zahra Monfared, Daniel Durstewitz
2024	A*	`conf`	Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction. NeurIPS Manuel Brenner, Christoph Jürgen Hemmer, Zahra Monfared, Daniel Durstewitz
2024	J	`jnl`	Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction. CoRR Manuel Brenner, Christoph Jürgen Hemmer, Zahra Monfared, Daniel Durstewitz
2024	J	`jnl`	Gradient-free training of recurrent neural networks. CoRR Erik Lien Bolager, Ana Cukarska, Iryna Burak, Zahra Monfared, Felix Dietrich
2024	A*	`conf`	Out-of-Domain Generalization in Dynamical Systems Reconstruction. ICML Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz
2024	J	`jnl`	Out-of-Domain Generalization in Dynamical Systems Reconstruction. CoRR Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz
2023	A*	`conf`	Bifurcations and loss jumps in RNN training. NeurIPS Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz
2023	J	`jnl`	Bifurcations and loss jumps in RNN training. CoRR Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz
2023	A*	`conf`	Generalized Teacher Forcing for Learning Chaotic Dynamics. ICML Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz
2023	J	`jnl`	Generalized Teacher Forcing for Learning Chaotic Dynamics. CoRR Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz
2022	A*	`conf`	On the difficulty of learning chaotic dynamics with RNNs. NeurIPS Jonas M. Mikhaeil, Zahra Monfared, Daniel Durstewitz
2022	A*	`conf`	Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. ICML Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz
2022	J	`jnl`	Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. CoRR Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz
2021	J	`jnl`	How to train RNNs on chaotic data? CoRR Zahra Monfared, Jonas M. Mikhaeil, Daniel Durstewitz
2021	A*	`conf`	Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies. ICLR Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz
2020	A*	`conf`	Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time. ICML Zahra Monfared, Daniel Durstewitz

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.