Qianhan Feng

11 papers A* 4Journal 7

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Large Model Enhancement. CVPR Qianhan Feng, Wenshuo Li, Tong Lin, Xinghao Chen
2025	J	`jnl`	CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment. CoRR Yakun Zhu, Zhongzhen Huang, Qianhan Feng, Linjie Mu, Yannian Gu, Shaoting Zhang, Qi Dou, Xiaofan Zhang
2025	J	`jnl`	Full-Stage Pseudo Label Quality Enhancement for Weakly-Supervised Temporal Action Localization. IEEE Trans. Circuits Syst. Video Technol. Qianhan Feng, Wenshuo Li, Tong Lin, Xinghao Chen
2025	J	`jnl`	UCAgents: Unidirectional Convergence for Visual Evidence Anchored Multi-Agent Medical Decision-Making. CoRR Qianhan Feng, Zhongzhen Huang, Yakun Zhu, Xiaofan Zhang, Qi Dou
2024	J	`jnl`	Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model. CoRR Qianhan Feng, Wenshuo Li, Tong Lin, Xinghao Chen
2024	A*	`conf`	BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning. AAAI Qianhan Feng, Lujing Xie, Shijie Fang, Tong Lin
2024	J	`jnl`	BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning. CoRR Qianhan Feng, Lujing Xie, Shijie Fang, Tong Lin
2024	J	`jnl`	Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization. CoRR Qianhan Feng, Wenshuo Li, Tong Lin, Xinghao Chen
2024	A*	`conf`	VCC-INFUSE: Towards Accurate and Efficient Selection of Unlabeled Examples in Semi-supervised Learning. IJCAI Shijie Fang, Qianhan Feng, Tong Lin
2024	J	`jnl`	VCC-INFUSE: Towards Accurate and Efficient Selection of Unlabeled Examples in Semi-supervised Learning. CoRR Shijie Fang, Qianhan Feng, Tong Lin
2021	A*	`conf`	Deep Homography for Efficient Stereo Image Compression. CVPR Xin Deng, Wenzhe Yang, Ren Yang, Mai Xu, Enpeng Liu, Qianhan Feng, Radu Timofte

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.