3 citations · 12 across the 15 of their papers we have counts for
15 papers
Learning Dynamic Belief Graphs for Theory-of-mind Reasoning
Ruxiao Chen, Xilei Zhao, Thomas J. Cova +2
Theory of Mind (ToM) reasoning with Large Language Models (LLMs) requires inferring how people's implicit, evolving beliefs shape what they seek and how they act under uncertainty…
Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports
Liangkai Zhou, Susu Xu, Shuqi Zhong +1
Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multiple related tasks where the stru…
Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach
Yilong Dai, Ziyi Wang, Chenguang Wang +4
Bikeability assessment is essential for advancing sustainable urban transportation and creating cyclist-friendly cities, and it requires incorporating users' perceptions of safety…
Wildfire Evacuation Analysis Using Facebook Data: Evidence from Palisades and Eaton Fires
Shangkun Jiang, Ruggiero Lovreglio, Thomas J. Cova +3
The growing frequency and intensity of wildfires pose serious threats to communities in wildland-urban interface regions. Understanding evacuation behavior is critical for effectiv…
From Image Generation to Infrastructure Design: a Multi-agent Pipeline for Street Design Generation
Chenguang Wang, Xiang Yan, Yilong Dai +2
Realistic visual renderings of street-design scenarios are essential for public engagement in active transportation planning. Traditional approaches are labor-intensive, hindering…
Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning
Yuran Sun, Susu Xu, Chenguang Wang +1
Big trajectory data hold great promise for human mobility analysis, but their utility is often constrained by the absence of critical traveler attributes, particularly sociodemogra…
Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference
Xuechun Li, Shan Gao, Susu Xu
Complex systems with intricate causal dependencies challenge accurate prediction. Effective modeling requires precise physical process representation, integration of interdependent…
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
Xuechun Li, Shan Gao, Runyu Gao +1
Post-earthquake hazard and impact estimation are critical for effective disaster response, yet current approaches face significant limitations. Traditional models employ fixed para…
Multi-class Seismic Building Damage Assessment from InSAR Imagery using Quadratic Variational Causal Bayesian Inference
Xuechun Li, Susu Xu
Interferometric Synthetic Aperture Radar (InSAR) technology uses satellite radar to detect surface deformation patterns and monitor earthquake impacts on buildings. While vital for…
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Ruxiao Chen, Chenguang Wang, Yuran Sun +2
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate res…
Spatial-variant causal Bayesian inference for rapid seismic ground failures and impacts estimation
Xuechun Li, Susu Xu
Rapid and accurate estimation of post-earthquake ground failures and building damage is critical for effective post-disaster responses. Progression in remote sensing technologies h…
Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach
Zhuoqun Xue, Xiaojian Zhang, David O. Prevatt +3
Accurately assessing building damage is critical for disaster response and recovery. However, many existing models for detecting building damage have poor prediction accuracy due t…
Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models
Chenguang Wang, Davis Engler, Xuechun Li +4
When a damaging earthquake occurs, immediate information about casualties is critical for time-sensitive decision-making by emergency response and aid agencies in the first hours a…
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery
Chenguang Wang, Yepeng Liu, Xiaojian Zhang +6
Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies pro…
Normalizing flow-based deep variational Bayesian network for seismic multi-hazards and impacts estimation from InSAR imagery
Xuechun Li, Paula M. Burgi, Wei Ma +3
Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate…