Qian Wang

14 papers Journal 14

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	A general positivity-preserving algorithm for implicit high-order finite volume schemes solving the Euler and Navier-Stokes equations. J. Comput. Phys. Qian-Min Huang, Hanyu Zhou, Yu-Xin Ren, Qian Wang
2024	J	`jnl`	Machine learning optimization of compact finite volume methods on unstructured grids. J. Comput. Phys. Chong-Bo Zhou, Qian Wang, Yu-Xin Ren
2023	J	`jnl`	Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs. CoRR Junming Duan, Qian Wang, Jan S. Hesthaven
2022	J	`jnl`	High-order compact finite volume schemes for solving the Reynolds averaged Navier-Stokes equations on the unstructured mixed grids with a large aspect ratio. J. Comput. Phys. Qian-Min Huang, Yu-Xin Ren, Qian Wang, Jianhua Pan
2021	J	`jnl`	Coupling kinetic and continuum using data-driven maximum entropy distribution. J. Comput. Phys. Mohsen Sadr, Qian Wang, M. Hossein Gorji
2021	J	`jnl`	High Order Finite Volume Schemes for Solving the Non-Conservative Convection Equations on the Unstructured Grids. J. Sci. Comput. Qian-Min Huang, Yu-Xin Ren, Qian Wang
2021	J	`jnl`	Physics-informed machine learning for reduced-order modeling of nonlinear problems. J. Comput. Phys. Wenqian Chen, Qian Wang, Jan S. Hesthaven, Chuhua Zhang
2020	J	`jnl`	A J. Comput. Phys. Wanai Li, Qian Wang, Yu-Xin Ren
2020	J	`jnl`	Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism. J. Comput. Phys. Qian Wang, Nicolò Ripamonti, Jan S. Hesthaven
2019	J	`jnl`	Compact high order finite volume method on unstructured grids IV: Explicit multi-step reconstruction schemes on compact stencil. J. Comput. Phys. Yu-Si Zhang, Yu-Xin Ren, Qian Wang
2019	J	`jnl`	Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem. J. Comput. Phys. Qian Wang, Jan S. Hesthaven, Deep Ray
2017	J	`jnl`	Compact high order finite volume method on unstructured grids III: Variational reconstruction. J. Comput. Phys. Qian Wang, Yu-Xin Ren, Jianhua Pan, Wanai Li
2016	J	`jnl`	Compact high order finite volume method on unstructured grids I: Basic formulations and one-dimensional schemes. J. Comput. Phys. Qian Wang, Yu-Xin Ren, Wanai Li
2016	J	`jnl`	Compact high order finite volume method on unstructured grids II: Extension to two-dimensional Euler equations. J. Comput. Phys. Qian Wang, Yu-Xin Ren, Wanai 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.