Greetings! I am Keshu Wu, a researcher working at the intersection of artificial intelligence, intelligent transportation, and cyber-physical systems. My research focuses on developing interactive and trustworthy intelligent systems that perceive, reason, and make decisions in complex physical environments. These systems aim to enable safe and coordinated interactions among humans, infrastructure, and autonomous agents, supporting the next generation of intelligent mobility and infrastructure systems.
My research specifically involves:
2) Digital Twin and Cyber-Physical Systems — building end-to-end digital twin pipelines that integrate data, simulation, and system optimization, while leveraging generative AI and LLM-based reasoning to synthesize scenarios and agent behaviors.
3) Spatiotemporal AI for Networked Systems — developing methods for spatiotemporal reconstruction, forecasting, and scalable spatial–temporal network analysis to enable data fusion and system understanding across large-scale dynamic networks.
My theoretical foundation draws on machine learning, generative AI, network science, and computational optimization, integrated with transportation engineering and cyber-physical systems, with the goal of improving transportation safety, efficiency, and everyday human mobility.
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Aug. 2024 - PresentTexas A&M University
Postdoctoral Research Associate -
Sep. 2019 - Jun. 2024University of Wisconsin-Madison
Research Assistant
Education
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Sep. 2019 - Jun. 2024University of Wisconsin-Madison
Ph.D. in Civil and Environmental EngineeringM.S. in Computer Sciences
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Sep. 2017 - Dec. 2018Carnegie Mellon University
M.S. in Civil and Environmental Engineering -
Sep. 2013 - Jul. 2017Southeast University
B.E. in Civil Engineering
News
Selected Publications (view all )
A Deep Learning Enabled Economical Informatization Framework with Sparsely Located Sensors
Wu, K., Zhou, Y., Li, X., Ye, X., Tu, Z.
Under Review (2025)
# inductive learning # hybrid sensor network # graph transformer
A Deep Learning Enabled Economical Informatization Framework with Sparsely Located Sensors
Wu, K., Zhou, Y., Li, X., Ye, X., Tu, Z.
Under Review 2025
# inductive learning # hybrid sensor network # graph transformer
V2XSynth: An LLM-Driven, Retrieval-Augmented Framework for Realistic V2X Scenario Synthesis
Wu, K., Zhang, H., Li, P., Gan, R., You, J., Tu, Z., Zhou, Y.
Under Review (2025)
# LLM # RAG # V2X # scenario generation
V2XSynth: An LLM-Driven, Retrieval-Augmented Framework for Realistic V2X Scenario Synthesis
Wu, K., Zhang, H., Li, P., Gan, R., You, J., Tu, Z., Zhou, Y.
Under Review 2025
# LLM # RAG # V2X # scenario generation
AI²-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics
Wu, K., Li, Z., Li, S., Ye, X., Lord, D., Zhou, Y.
Under Review (2025)
# active safety # vehicle dynamics # hypergraph
AI²-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics
Wu, K., Li, Z., Li, S., Ye, X., Lord, D., Zhou, Y.
Under Review 2025
# active safety # vehicle dynamics # hypergraph
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning
Wu, K., Zhou, Y., Shi, H., Lord, D., Ran, B., Ye, X.
Transportation Research Part C: Emerging Technologies (2025)
# hypergraph # interaction # multi-modality
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning
Wu, K., Zhou, Y., Shi, H., Lord, D., Ran, B., Ye, X.
Transportation Research Part C: Emerging Technologies 2025
# hypergraph # interaction # multi-modality
V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors
Wu, K., Li, P., Zhou, Y., Gan, R., You, J., Cheng, Y., Zhu, J., Parker, S. T., Noyce, D., Ran, B., Tu, Z.
Under Review (2025)
# LLM # V2X
V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors
Wu, K., Li, P., Zhou, Y., Gan, R., You, J., Cheng, Y., Zhu, J., Parker, S. T., Noyce, D., Ran, B., Tu, Z.
Under Review 2025
# LLM # V2X
A Digital Twin Framework for Physical-Virtual Integration in Enhancing Connected Vehicle Corridor Data Pipeline
Wu, K., Li, P., Cheng, Y., Parker, S. T., Noyce, D.,, Ran, B., Ye, X.
IEEE Transactions on Intelligent Transportation Systems (2025)
# digital twin # V2X # connected vehicle corridor
A Digital Twin Framework for Physical-Virtual Integration in Enhancing Connected Vehicle Corridor Data Pipeline
Wu, K., Li, P., Cheng, Y., Parker, S. T., Noyce, D.,, Ran, B., Ye, X.
IEEE Transactions on Intelligent Transportation Systems 2025
# digital twin # V2X # connected vehicle corridor
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction Using Diffusion Graph Convolutional Networks
Wu, K., Zhou, Y., Shi, H., Li, X., Ran, B.
IEEE Transactions on Intelligent Vehicles (2024)
# GNN # interaction # mutli-modality
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction Using Diffusion Graph Convolutional Networks
Wu, K., Zhou, Y., Shi, H., Li, X., Ran, B.
IEEE Transactions on Intelligent Vehicles 2024
# GNN # interaction # mutli-modality