Gang Ren

16 papers Journal 15Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
Dynamic Spatiotemporal Graph Convolutional Neural Network Based on Congestion Propagation for Traffic Prediction.
IEEE Trans. Intell. Transp. Syst.
Wenxie Lin, Zhe Zhang, Ruiyu Wang, Gang Ren, Yangzhen Zhao, Jianhua Song, Qi Cao
2026 J jnl
High-resolution traffic flow estimation for urban road network: incorporating traffic flow model into deep learning.
Expert Syst. Appl.
Jianhua Song, Zhe Zhang, Gang Ren, Jiefei Zhang, Jingfeng Ma
2026 J jnl
Leveraging Roadside Sensor Observations for Accurate and Efficient Vehicle Trajectory Reconstruction.
IEEE Internet Things J.
Yue Deng, Qi Cao, Gang Ren, Changjian Wu, Jianhua Song
2025 J jnl
Deciphering the Crash Mechanisms in Autonomous Vehicle Systems via Explainable AI.
Syst.
Zhe Zhang, Wentao Wu, Qi Cao, Jianhua Song, Jingfeng Ma, Gang Ren, Changjian Wu
2025 J jnl
Interaction-aware vehicle trajectory prediction using spatial-temporal dynamic graph neural network.
Knowl. Based Syst.
Ruiyu Wang, Wenxie Lin, Gang Ren, Qi Cao, Zhe Zhang, Yue Deng
2025 J jnl
MGCN: Mamba-integrated spatiotemporal graph convolutional network for long-term traffic forecasting.
Knowl. Based Syst.
Wenxie Lin, Zhe Zhang, Gang Ren, Yangzhen Zhao, Jingfeng Ma, Qi Cao
2025 J jnl
Monitoring of operational resilience on urban road network: A Shaoxing case study.
Reliab. Eng. Syst. Saf.
Jianwei Du, Gang Ren, Jialei Cui, Qi Cao, Jian Wang, Chenyang Wu, Jiefei Zhang
2025 J jnl
Passenger Switch Behavior and Decision Mechanisms in Multimodal Public Transportation Systems.
Syst.
Zhe Zhang, Wenxie Lin, Tongyu Hu, Qi Cao, Jianhua Song, Gang Ren, Changjian Wu
2025 J jnl
Reconstructing fully-sampled vehicle trajectories with fragmented observations from connected and automated vehicles.
Expert Syst. Appl.
Yue Deng, Qi Cao, Gang Ren, Changjian Wu, Shuichao Zhang
2025 J jnl
Soft Actor-Critic Deep Reinforcement Learning for Train Timetable Collaborative Optimization of Large-Scale Urban Rail Transit Network Under Dynamic Demand.
IEEE Trans. Intell. Transp. Syst.
Longhui Wen, Liyang Hu, Wei Zhou, Gang Ren, Ning Zhang
2025 J jnl
State-Space and Multi-Scale Convolutional Generative Adversarial Network for Traffic Flow Forecasting.
Syst.
Wenxie Lin, Zhe Zhang, Yangzhen Zhao, Jinyu Zhang, Gang Ren
2024 J jnl
Interpretable Prediction of Pedestrian Crossing Intention: Fusion of Human Skeletal Information in Natural Driving Scenarios.
IEEE Trans. Intell. Transp. Syst.
Haojie Li, Yuan Jin, Gang Ren
2024 — conf
Vehicle Re-identification with a Pose-Aware Discriminative Part Learning Model.
PRCV (13)
Ya Sun, Jianfeng Lu, Minxian Li, Gang Ren, Jingfeng Ma
2022 J jnl
A Stochastic Dynamic Traffic Assignment Model for Emergency Evacuations That Considers Background Traffic.
IEEE Intell. Transp. Syst. Mag.
Tao Zhang, Gang Ren, Gang Cheng, Yang Yang, Minjie Jin
2022 J jnl
Map Matching for Sparse Automatic Vehicle Identification Data.
IEEE Trans. Intell. Transp. Syst.
Qi Cao, Gang Ren, Dawei Li, Haojie Li, Jiangshan Ma
2021 J jnl
Multi-Modal Combined Route Choice Modeling in the MaaS Age Considering Generalized Path Overlapping Problem.
IEEE Trans. Intell. Transp. Syst.
Dawei Li, Min Yang, Cheng-Jie Jin, Gang Ren, Xianglong Liu, Haode Liu

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.