Zhaonan Wang

Orcid: 0000-0002-2613-9727

Affiliations:
  • New York University (NYU), Shanghai, China
  • University of Illinois Urbana-Champaign, IL, USA (former)
  • University of Tokyo, Japan (Ph.D.)
  • Peking University, Beijing, China (former)


According to our database1, Zhaonan Wang authored at least 25 papers between 2013 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Deep Learning and Foundation Models for Weather Prediction: A Survey.
CoRR, January, 2025

2024
Forecasting Citywide Crowd Transition Process via Convolutional Recurrent Neural Networks.
IEEE Trans. Mob. Comput., May, 2024

Learning spatio-temporal dynamics on mobility networks for adaptation to open-world events.
Artif. Intell., 2024

Geospatial Topological Relation Extraction from Text with Knowledge Augmentation.
Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

2023
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction.
IEEE Trans. Knowl. Data Eng., 2023

Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates.
CoRR, 2023

Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster.
Proceedings of the ACM Web Conference 2023, 2023

Towards an Event-Aware Urban Mobility Prediction System.
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Geo-Foundation Models: Reality, Gaps and Opportunities.
Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 2023

Geospatial Knowledge Hypercube.
Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 2023

MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

Spatio-Temporal Meta-Graph Learning for Traffic Forecasting.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System.
ACM Trans. Intell. Syst. Technol., 2022

DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction (Extended abstract).
Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Yahoo! Bousai Crowd Data: A Large-Scale Crowd Density and Flow Dataset in Tokyo and Osaka.
Proceedings of the IEEE International Conference on Big Data, 2022

Event-Aware Multimodal Mobility Nowcasting.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Transfer Urban Human Mobility via POI Embedding over Multiple Cities.
Trans. Data Sci., 2021

Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks.
Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020
CoolPath: An Application for Recommending Pedestrian Routes with Reduced Heatstroke Risk.
Proceedings of the Web and Wireless Geographical Information Systems, 2020

2019
VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction.
CoRR, 2019

DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

2013
The Chang E-3 landing and working area selecting: Based on the lunar digital terrain model.
Proceedings of the 21st International Conference on Geoinformatics, 2013


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