Dr. Danhuai Guo
Associate Professor
Director of Spatio-Temporal Data Intelligence Lab
Email: gdh@mail.buct.cn
Background
Danhui Guo is a Professor and Ph.D. Supervisor. He received his M.S. degree from Wuhan University and his Ph.D. degree from the Graduate University of the Chinese Academy of Sciences. He is currently the Director of the Spatio-Temporal Data Intelligence Laboratory at Beijing University of Chemical Technology. He has been selected for a provincial-level high-level talent program. He is a Senior Member of the China Computer Federation (CCF), a Corresponding Executive Committee Member of the CCF Big Data Technical Committee, an Executive Committee Member of ACM SIGSPATIAL China, and a Committee Member of the Hybrid Intelligence Technical Committee of the Chinese Association for Artificial Intelligence. He serves as an Editorial Board Member of the Journal of Safety Science and Resilience, and as Associate Editor and Action Editor of Geoinformatica.
Professor Guo has presided over more than 10 major projects, including three General Programs of the National Natural Science Foundation of China (NSFC), an NSFC Major Research Plan Cultivation Project, sub-projects of the Ministry of Science and Technology’s Torch Program, and sub-projects of the CAS 12th Five-Year Plan Informatics Project.
He has published nearly 100 academic papers in top-tier international journals and conferences, including The Lancet, Nature Communications, WWW Journal, Geoinformatica, ACM TIST, NeuroComputing, Journal of Computer Science and Technology, DASFAA, and ACM SIGSPATIAL. He has authored two monographs and holds over 10 authorized invention patents.
Areas of Research of Expertise
High-Performance Geocomputing
Spatio-Temporal Geographic Intelligence (GeoAI)
Anomaly Detection & Large Model Methods
Teaching
Undergraduate Teaching
Nosql databases
Nosql databases Experiment
Introduction to Big Data Science and Technology
Computer System Architecture
Principles of Data Mining
Postgraduate Teaching
Data Analysis and Data Intelligence
Research
Funded Research Projects
The National Natural Science Foundation of China:
Key Technologies for Spatial Scene Generation Using Generative Adversarial Networks with Embedded Spatial Constraints
Research on Key Technologies for Spatial Scene Similarity Matching Based on Deep Convolutional Neural Networks
Main Achievements & Awards
In High-Performance Geocomputing: He conducts in-depth research focusing on spatial cognition, scene generation, and representation learning. His related findings have been published in internationally renowned journals such as ACM Transactions on Intelligent Systems and Technology (ACM TIST), International Journal of Applied Earth Observation and Geoinformation, Journal of Safety Science and Resilience, Journal of Big Data, and ISPRS International Journal of Geo-Information.
In Aerospace & High-End Equipment: His research group has constructed intelligent diagnosis and prediction models, developing core technologies such as sensor streaming data analysis, fault knowledge graphs, and dynamic threshold detection. These technologies have been successfully applied to the anomaly detection and life assessment of spacecraft propulsion systems. His work has provided solid technical support for the ground testing and Operations & Maintenance (O&M) assurance of major national aerospace missions, including the BeiDou Navigation Satellite System, the Chang'e Lunar Exploration Program, and the China Space Station.
1st Place, ACM GIS Cup (2014)
3rd Prize (2015) and 2nd Prize (2018), China VIS & Vast Challenge
1st Prize, SpatialDI (2020)
Five Representative Publications
Yu S, Guo D, Fu Y, et al. EventFormer: a hierarchical neural point process framework for spatio-temporal clustering events prediction[J]. Journal of Big Data, 2025, 12(1): 162.
Yu, S.; Zhu, J.; Li, J.; Li, X.; Wang, K.; Tu, J.; Guo, D. SceneDiffusion: Scene Generation Model Embedded with Spatial Constraints. ISPRS International Journal of Geo-Information, 2025, 14(7): 250.
Guo D, Yu Y, Ge S, et al. SpatialScene2Vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluation[J]. International Journal of Applied Earth Observation and Geoinformation, 2024, 128: 103743.
Guo D, Chen H, Wu R, et al. AIGC challenges and opportunities related to public safety: a case study of ChatGPT[J]. Journal of Safety Science and Resilience, 2023, 4(4): 329-339.
Dou Z, Guo D. DPSTCN: Dynamic Pattern-Aware Spatio-Temporal Convolutional Networks for Traffic Flow Forecasting[J]. ISPRS International Journal of Geo-Information, 2024, 14(1): 10.