Yang Zhang

14 papers Journal 11Unranked 2

Year	Rank	Type	Title / Venue / Authors
2021	J	`jnl`	ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs. Geo spatial Inf. Sci. Nilufer Sari Aslam, Mohamed R. Ibrahim, Tao Cheng, Huanfa Chen, Yang Zhang
2021	J	`jnl`	Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London. Ann. GIS Nilufer Sari Aslam, Di Zhu, Tao Cheng, Mohamed R. Ibrahim, Yang Zhang
2020	J	`jnl`	A Deep Cross-Modality Hashing Network for SAR and Optical Remote Sensing Images Retrieval. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. Wei Xiong, Zhenyu Xiong, Yang Zhang, Yaqi Cui, Xiangqi Gu
2020	J	`jnl`	A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data. IEEE Trans. Intell. Transp. Syst. Yang Zhang, Tao Cheng
2020	J	`jnl`	A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes. Int. J. Geogr. Inf. Sci. Yibin Ren, Huanfa Chen, Yong Han, Tao Cheng, Yang Zhang, Ge Chen
2020	J	`jnl`	A novel residual graph convolution deep learning model for short-term network-based traffic forecasting. Int. J. Geogr. Inf. Sci. Yang Zhang, Tao Cheng, Yibin Ren, Kun Xie
2020	J	`jnl`	Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events. Comput. Environ. Urban Syst. Yang Zhang, Tao Cheng
2020	—	`—`	Graph deep learning models for network-based spatio-temporal data forecasting: from dense to sparse. Yang Zhang
2020	—	`conf`	Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision Using Graph Convolutional Networks. MICCAI (2) Junyan Wu, Hao Jiang, Xiaowei Ding, Anudeep Konda, Jin Han, Yang Zhang, Qian Li
2020	J	`jnl`	Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks. CoRR Junyan Wu, Hao Jiang, Xiaowei Ding, Anudeep Konda, Jin Han, Yang Zhang, Qian Li
2020	J	`jnl`	You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data. Comput. Environ. Urban Syst. Yang Zhang, Nilufer Sari Aslam, Juntao Lai, Tao Cheng
2019	J	`jnl`	A graph deep learning method for short-term traffic forecasting on large road networks. Comput. Aided Civ. Infrastructure Eng. Yang Zhang, Tao Cheng, Yibin Ren
2019	J	`jnl`	Deep spatio-temporal residual neural networks for road-network-based data modeling. Int. J. Geogr. Inf. Sci. Yibin Ren, Tao Cheng, Yang Zhang
2017	—	`conf`	Robust system portfolio selection with multi-function requirements and system instability. SysCon Boyuan Xia, Yajie Dou, Qingsong Zhao, Bingfeng Ge, Yang Zhang

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.