Raeid Saqur

17 papers A* 3Journal 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains. CoRR Raeid Saqur, Christoph Bergmeir, Blanka Horvath, Daniel Schmidt, Frank Rudzicz, Terry Lyons
2025	J	`jnl`	Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework. CoRR Nicholas Vinden, Raeid Saqur, Zining Zhu, Frank Rudzicz
2025	A*	`conf`	Filtered not Mixed: Filtering-Based Online Gating for Mixture of Large Language Models. ICLR Raeid Saqur, Anastasis Kratsios, Florian Krach, Yannick Limmer, Blanka Horvath, Frank Rudzicz
2024	J	`jnl`	Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models. CoRR Raeid Saqur, Anastasis Kratsios, Florian Krach, Yannick Limmer, Jacob-Junqi Tian, John Willes, Blanka Horvath, Frank Rudzicz
2024	J	`jnl`	L(u)PIN: LLM-based Political Ideology Nowcasting. CoRR Ken Kato, Annabelle Purnomo, Christopher Cochrane, Raeid Saqur
2024	J	`jnl`	NIFTY Financial News Headlines Dataset. CoRR Raeid Saqur, Ken Kato, Nicholas Vinden, Frank Rudzicz
2024	J	`jnl`	Reality Only Happens Once: Single-Path Generalization Bounds for Transformers. CoRR Yannick Limmer, Anastasis Kratsios, Xuwei Yang, Raeid Saqur, Blanka Horvath
2024	J	`jnl`	What Teaches Robots to Walk, Teaches Them to Trade too - Regime Adaptive Execution using Informed Data and LLMs. CoRR Raeid Saqur
2023	A*	`conf`	Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset. NeurIPS Saeid Alavi Naeini, Raeid Saqur, Mozhgan Saeidi, John M. Giorgi, Babak Taati
2023	J	`jnl`	Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset. CoRR Saeid Alavi Naeini, Raeid Saqur, Mozhgan Saeidi, John M. Giorgi, Babak Taati
2022	J	`jnl`	Reward Learning using Structural Motifs in Inverse Reinforcement Learning. CoRR Raeid Saqur
2020	J	`jnl`	CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes. CoRR Raeid Saqur, Ameet Deshpande
2020	J	`jnl`	Ethics of Artificial Intelligence in Surgery. CoRR Frank Rudzicz, Raeid Saqur
2020	A*	`conf`	Multimodal Graph Networks for Compositional Generalization in Visual Question Answering. NeurIPS Raeid Saqur, Karthik Narasimhan
2018	J	`jnl`	CapsGAN: Using Dynamic Routing for Generative Adversarial Networks. CoRR Raeid Saqur, Sal Vivona
2018	J	`jnl`	H4-Writer: A Text Entry Method Designed For Gaze Controlled Environment with Low KSPC and Spatial Footprint. CoRR Raeid Saqur
2018	J	`jnl`	ReGAN: RE[LAX\|BAR\|INFORCE] based Sequence Generation using GANs. CoRR Aparna Balagopalan, Satya Gorti, Mathieu Ravaut, Raeid Saqur

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.