Ruwen Qin

Associate Professor

Stony Brook University

United States

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About

Ruwen Qin is an Associate Professor at Stony Brook University in the United States. His research areas include structural engineering, deep learning applications in traffic safety, and automated inspection systems. His recent articles focus on understanding structural failures in extreme weather events, developing machine learning models for infrastructure inspection, and enhancing safety measures in transportation. Dr. Qin's work is pivotal in advancing technologies for risk assessment and action recognition in various engineering domains.

Articles (15)

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2025

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2022

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2022

Collaborators (3)

Genda Chen

Ph.D./Professor and Abbett Distinguished Chair in Civil Engineering

Missouri University of Science and Technology

UNITED STATES

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Zhaozheng Yin

Stony Brook University

UNITED STATES

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Shanshan Yao

Assistant Professor

Stony Brook University

UNITED STATES

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Ruwen Qin

Research Interests

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Articles (15)

Understanding the relationship between structural failure and fatalities in Tornadoes: A quantitative investigation of the 2021 Midwest Tornado Outbreak

A surface electromyography–based deep learning model for guiding semi‐autonomous drones in road infrastructure inspection

Weakly‐supervised structural component segmentation via scribble annotations

Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos

Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automated structural condition assessment in visual inspection

A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images

Jointly-Learnt Networks for Future Action Anticipation via Self-Knowledge Distillation and Cycle Consistency

Simulation Study of Passing Drivers’ Responses to the Autonomous Truck-Mounted Attenuator System in Road Maintenance

An individualized system of skeletal data-based CNN classifiers for action recognition in manufacturing assembly

A multi-tasking model of speaker-keyword classification for keeping human in the loop of drone-assisted inspection

An Attention-guided Multistream Feature Fusion Network for Early Localization of Risky Traffic Agents in Driving Videos

A Virtual-Reality-Based Training and Assessment System for Bridge Inspectors With an Assistant Drone

A Dynamic Spatial-Temporal Attention Network for Early Anticipation of Traffic Accidents

Toward Explainable Artificial Intelligence for Early Anticipation of Traffic Accidents

A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos

Collaborators (3)

Genda Chen

Zhaozheng Yin

Shanshan Yao

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