Jingtao Ding

Biography

Jingtao Ding is currently a Postdoctoral Research Fellow in the Department of Electronic Engineering at Tsinghua University. He obtained his Ph.D. Degree (advised by Prof. Yong Li and Prof. Depeng Jin) and Bachelor’s Degree from the same department in 2020 and 2015, respectively. During the years 2020 and 2022, he worked as an applied researcher at Wechat, the largest SNS company in China. His current research focuses primarily on data mining (spatio-temporal data and user behavior), complex netowrks, AI for science, etc., with over 40 publications in top-tier venues on data science and machine learning, including KDD, WWW, NeurIPS, ICLR, etc..

Research Interests

Spatio-temporal Data Mining: spatio-temporal foundation model, diffusion model
AI for Complex Systems: learning symbolic model, physics-informed simulation, data-driven control, data augmentation
User Behavior Modeling: large-language-model empowered prediction, cloud-device collaborative learning

News

[New!] We are about to organize a lecture-style tutorial at WWW2025. The topic is “AI + Network Science”. Please check our website. See you in Sydney!
[New!] The journal version of UniST is accepted by IEEE TKDE, where we extend its use to graph-based spatiotemporal data.
[New!] Our DeepMobility has been accepted by PNAS Nexus. We present a deep generative collaboration network capable of reproducing various human mobility laws previously discovered by physicists.
[New!] Two papers on symbolic regression and OD flow generation benchmark are accepted by ICLR 2025.
[New!] Two papers on diffusion model for generating urban mobility and profile-conditioned trajectories are accepted by WWW 2025 and ACM TKDD.
[New!] We are happy to share our recent progress on spatio-temporal foundation model, extending UniST from grid-based data to flow-based data (UniFlow). Please check our released project here.
[New!] We release a comprehensive survey about world model.
[Dec. 2024] I am invited to give a talk about scientific discovery in urban complex system at “AI for Science” Frontier Forum in CCF ChinaData 2024.
[Oct. 2024] We have succussfully held The Third “AI+Complex Systems” Frontier Forum in China National Computer Congress (CNCC2024).
[Sep. 2024] I am invited to give a lecture about data-driven urban complex system modeling at Social Intelligence Conference.
[August 2024] I am invited to give a talk about knowledge-informed generative learning for spatiotemporal data in KDD DeepSpatial’24 and MILETS’24 workshop.
[August 2024] Three papers on spatio-temporal foundation model, complex network resilience prediction and LLM-based intent prediction are going to be presented at KDD 2024.
[July 2024] We are happy to share our recent progress on extending the one-for-all foundation model for urban spatio-temporal prediction (UniST). The code, pretrained model weights and data (>130M points) are released here.
[June 2024] Our survey paper on origin-destination flows (a critical form of urban dynamics) has been accepted by ACM CSUR. We have also released a benchmark for three types of OD modeling tasks.
[May 2024] Our paper about spatio-temporal few-shot learning with diffusion model is accepted to ICLR 2024. Feel free to try our code on cross-city transfer tasks.
[Jan. 2024] We are excited to share our recent survey on AI for complex networks. A short version in Chinese has been published.
[Oct. 2023] We have succussfully held The Second “AI+Complex Systems” Frontier Forum in China National Computer Congress (CNCC2023).
[Dec. 2022] We have succussfully held The First “AI+Complex Systems” Frontier Forum in China National Computer Congress (CNCC2022).

Publications

Preprint

Artificial Intelligence for Complex Network: Potential, Methodology and Application

Jingtao Ding, C. Liu, Y. Zheng, Y. Zhang, Z. Yu, R. Li, H. Chen, J. Piao, H. Wang, J. Liu, Y. Li

arXiv:2402.16887, 2024.

Understanding World or Predicting Future? A Comprehensive Survey of World Models

Jingtao Ding, Y. Zhang, Y. Shang, Y. Zhang, Z. Zong, J. Feng, Y. Yuan, H. Su, N. Li, N. Sukiennik, F. Xu, Y. Li

arXiv:2411.14499, 2024.

A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History

E. Xu, C. Rong, Jingtao Ding, Y. Li

arXiv:2501.06485, 2025.

Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction

Z. Sheng, Y. Yuan, Jingtao Ding, Y. Li

arXiv:2501.13794, 2025.

A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction [Code]

Y. Yuan, Jingtao Ding, C. Han, D. Jin, Y. Li

arXiv:2411.12972, 2024.

Urbandit: A foundation model for open-world urban spatio-temporal learning

Y. Yuan, C. Han, Jingtao Ding, D. Jin, Y. Li

arXiv:2411.12164, 2024.

Beyond imitation: Generating human mobility from context-aware reasoning with large language models

C. Shao, F. Xu, B. Fan, Jingtao Ding, Y. Yuan, M. Wang, Y. Li

arXiv:2402.09836, 2024.

UrbanWorld: An Urban World Model for 3D City Generation

Y. Shang, J. Chen, H. Fan, Jingtao Ding, J. Feng, Y. Li

arXiv:2407.11965, 2024.

City-wide origin-destination matrix generation via graph denoising diffusion [Code]

C Rong, J Ding, Z Liu, Y Li

arXiv:2306.04873, 2023.

Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling

Y. Liu, S. Yang, Jingtao Ding, Q. Yao, Y. Li

arXiv:22411.06191, 2024.

The Year of 2025

Learning the complexity of urban mobility with deep generative collaboration network [Code]

Y Yuan, Jingtao Ding, D. Jin, , Y. Li

PNAS Nexus, 2025.

A Universal Pre-training and Prompting Framework for General Urban Spatio-Temporal Prediction [Code]

Y. Yuan, Jingtao Ding, J. Feng, D. Jin, Y. Li

IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025.

Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length [Code]

Z. Yu, Jingtao Ding, Y. Li, D. Jin

International Conference on Learning Representations (ICLR), 2025.

A Large-scale Dataset and Benchmark for Commuting Origin-Destination Flow Generation [Code]

C. Rong, Jingtao Ding, Y. Liu, Y. Li

International Conference on Learning Representations (ICLR), 2025.

Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors [Code]

Y. Zhang, Y. Yuan, Jingtao Ding, J. Yuan, Y. Li

ACM The Web Conference (TheWebConf/WWW), 2025.

STTF: A Spatiotemporal Transformer Framework for Multi-task Mobile Network Prediction [Code]

J. Gong, Y. Liu, T. Li, Jingtao Ding, Z. Wang, D. Jin

IEEE Transactions on Mobile Computing (TMC), 2025.

The Year of 2024

人工智能驱动的复杂系统研究前沿

丁璟韬，徐丰力，孙浩，严钢，胡延庆，李勇，周涛

电子科技大学学报, 2024.

An interdisciplinary survey on origin-destination flows modeling: Theory and techniques [Code]

C. Rong, Jingtao Ding, Y. Li

ACM Computing Surveys (CSUR), 2024.

Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives

C. Gao, X. Lan, N. Li, Y. Yuan, Jingtao Ding, Z. Zhou, F. Xu & Y. Li

Humanities and Social Sciences Communications, 2024

Controllable Human Trajectory Generation Using Profile-Guided Latent Diffusion [Code]

Y. Song, Jingtao Ding, J. Yuan, Q. Liao, Y. Li

ACM Transactions on Knowledge Discovery from Data (TKDD), 2024.

Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning [Code]

H. Su, Y. Zheng, Jingtao Ding, D. Jin, Y. Li

ACM International Conference on Advances in Geographic Information Systems (SigSpatial), 2024.

UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction [Code]

Y. Yuan, Jingtao Ding, J. Feng, D. Jin, Y. Li

ACM Conference on Knowledge Discovery and Data Mining (KDD), 2024.

TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics [Code]

C. Liu, Jingtao Ding, Y. Song, Y. Li

ACM Conference on Knowledge Discovery and Data Mining (KDD), 2024.

A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction

J. Gong, Jingtao Ding, F. Meng, G. Chen, H. Chen, S. Zhao, H. Lu, Y. Li

ACM Conference on Knowledge Discovery and Data Mining (KDD), 2024.

Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation [Code]

Y. Yuan, C. Shao, Jingtao Ding, D. Jin, Y. Li

International Conference on Learning Representations (ICLR), 2024.

Social physics informed diffusion model for crowd simulation [Code]

H. Chen, Jingtao Ding, Y. Li, Y. Wang, X.-P. Zhang

AAAI Conference on Artificial Intelligence (AAAI), 2024.

Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data [Code]

J. Zeng, Y. Liu, Jingtao Ding, J. Yuan, Y. Li

AAAI Conference on Artificial Intelligence (AAAI), 2024.

Fully Decoupling Trajectory and Scene Encoding for Lightweight Heatmap-oriented Trajectory Prediction [Code]

R. Huang, Jingtao Ding, M. Pagnucco, Y. Song

IEEE Robotics and Automation Letters (RA-L), 2024.

Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic Information [Code]

C. Rong, Z. Liu, Jingtao Ding, Y. Li

ACM Transactions on Knowledge Discovery from Data (TKDD), 2024.

Generating Daily Activities with Need Dynamics [Code]

Y. Yuan, Jingtao Ding, H. Wang, D. Jin

ACM Transactions on Intelligent Systems and Technology (TIST), 2024.

Rumor Mitigation in Social Media Platforms with Deep Reinforcement Learning [Code]

H. Su, Y. Zheng, Jingtao Ding, D. Jin, Y. Li

Companion Proceedings of the ACM on Web Conference (WWW Poster), 2024.

MetroGNN: Metro Network Expansion with Reinforcement Learning [Code]

H. Su, Y. Zheng, Jingtao Ding, D. Jin, Y. Li

Companion Proceedings of the ACM on Web Conference (WWW Poster), 2024.

The Year of 2023

Spatio-temporal Diffusion Point Processes [Code]

Y. Yuan, Jingtao Ding, C. Shao, D. Jin, Y. Li

ACM Conference on Knowledge Discovery and Data Mining (KDD), 2023.

Road planning for slums via deep reinforcement learning [Code]

Y. Zheng, H. Su, Jingtao Ding, D. Jin, Y. Li

ACM Conference on Knowledge Discovery and Data Mining (KDD), 2023.

Learning Symbolic Models for Graph-structured Physical Mechanism

H. Shi, Jingtao Ding, Y. Cao, Q. Yao, L. Liu, Y. Li

International Conference on Learning Representations (ICLR), 2023.

Urbankg: An urban knowledge graph system

Y. Liu, Jingtao Ding, Y. Li

ACM Transactions on Intelligent Systems and Technology (TIST), 2023.

Hierarchical knowledge graph learning enabled socioeconomic indicator prediction in location-based social network [Code]

Z. Zhou, Y. Liu, Jingtao Ding, D. Jin, Y. Li

ACM The Web Conference (TheWebConf/WWW), 2023.

Knowledge-infused contrastive learning for urban imagery-based socioeconomic prediction [Code]

Y. Liu, X. Zhang, Jingtao Ding, Y. Xi, Y. Li

ACM The Web Conference (TheWebConf/WWW), 2023.

Learning to simulate daily activities via modeling dynamic human needs [Code]

Y. Yuan, H. Wang, Jingtao Ding, D. Jin, Y. Li

ACM The Web Conference (TheWebConf/WWW), 2023.

Robust preference-guided denoising for graph based social recommendation [Code]

Y. Quan, Jingtao Ding, C. Gao, L. Yi, D. Jin, Y. Li

ACM The Web Conference (TheWebConf/WWW), 2023.

Towards generative modeling of urban flow through knowledge-enhanced denoising diffusion [Code]

Z. Zhou, Jingtao Ding, Y. Liu, D. Jin, Y. Li

ACM International Conference on Advances in Geographic Information Systems (SigSpatial), 2023.

KnowSite: Leveraging Urban Knowledge Graph for Site Selection [Code]

Y. Liu, Jingtao Ding, Y. Li

ACM International Conference on Advances in Geographic Information Systems (SigSpatial), 2023.

Alleviating Video-length Effect for Micro-video Recommendation [Code]

Y. Quan, Jingtao Ding, C. Gao, N. Li, L. Yi, D. Jin, Y. Li

ACM Transactions on Information Systems (TOIS), 2023.

GODDAG: generating origin-destination flow for new cities via domain adversarial training

C. Rong, J Feng, Jingtao Ding

IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023.

A satellite imagery dataset for long-term sustainable development in united states cities

Y. Xi, Y. Liu, T. Li, Jingtao Ding, Y. Zhang, S. Tarkoma, Y. Li, P. Hui

Scientific data, 2023.

The Year of 2022

Activity trajectory generation via modeling spatiotemporal dynamics [Code]

Y. Yuan, Jingtao Ding, H. Wang, D. Jin, Y. Li

ACM Conference on Knowledge Discovery and Data Mining (KDD, health day), 2022.

Dvr: Micro-video recommendation optimizing watch-time-gain under duration bias [Code]

Y. Zheng, C. Gao, Jingtao Ding, L. Yi, D. Jin, Y. Li, M. Wang

ACM International Conference on Multimedia (MM), 2022.

PlatoGL: Effective and scalable deep graph learning system for graph-enhanced real-time recommendation

D. Lin, S. Sun, Jingtao Ding, X. Ke, H. Gu, X. Huang, C. Song, X. Zhang, L. Yi, J. Wen, C. Chen

ACM International Conference on Information & Knowledge Management (CIKM), 2022.

Causal learning empowered OD prediction for urban planning

J. Zeng, G. Zhang, C. Rong, Jingtao Ding, J. Yuan, Y. Li

ACM International Conference on Information & Knowledge Management (CIKM), 2022.

Item-provider co-learning for sequential recommendation

L. Chen, Jingtao Ding, M. Yang, C. Li, C. Song, L. Yi

ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR, short paper), 2022.

The Year of 2020

Simplify and robustify negative sampling for implicit collaborative filtering [Code]

Jingtao Ding, Y. Quan, Q. Yao, Y. Li, D. Jin

Advances in Neural Information Processing Systems (NeurIPS), 2020.

Learning from hometown and current city: Cross-city POI recommendation via interest drift and transfer learning [Code]

Jingtao Ding, G. Yu, Y. Li, D. Jin, H. Gao

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/Ubicomp), 2020.

Improving implicit recommender systems with auxiliary data [Code]

Jingtao Ding, G. Yu, Y. Li, X. He, D. Jin

ACM Transactions on Information Systems (TOIS), 2020.

Representative Negative Instance Generation for Online Ad Targeting

Y. Quan, Jingtao Ding, D. Jin, J. Yang, X. Zhou, Y. Li,

ACM International Conference on Information & Knowledge Management (CIKM, short paper), 2020.

The Year of 2019 and Before

Reinforced Negative Sampling for Recommendation with Exposure Data [Code]

Jingtao Ding, Y. Quan, X. He, Y. Li, D. Jin

International Joint Conference on Artificial Intelligence (IJCAI), 2019.

Sampler design for bayesian personalized ranking by leveraging view data [Code]

Jingtao Ding, G. Yu, X. He, F. Feng, Y. Li, D. Jin

IEEE transactions on knowledge and data engineering (TKDE), 2019.

Improving Implicit Recommender Systems with View Data [Code]

Jingtao Ding, G. Yu, X. He, Y. Quan, Y. Li, T.-S. Chua, D. Jin, J. Yu

International Joint Conference on Artificial Intelligence (IJCAI), 2018.

An improved sampler for bayesian personalized ranking by leveraging view data [Code]

Jingtao Ding, F. Feng, X. He, G. Yu, Y. Li, D. Jin

Companion proceedings of the the web conference (WWW Poster), 2018, Best Poster Award.

Click versus share: A feature-driven study of micro-video popularity and virality in social media

Jingtao Ding, Y. Li, Y. Li, D. Jin

SIAM International Conference on Data Mining (SDM), 2018.

Social Weak-tie Assisted Cross-domain Short Video Recommendation

X. Wang, C. Gao, Jingtao Ding, L. Hu, Y. Hu, Y. Li, D. Jin

IEEE International Wireless Communications & Mobile Computing Conference (IWCMC), 2019, Best Paper Award.

Beyond the click: A first look at the role of a microblogging platform in the Web ecosystem

Jingtao Ding, Z. Liu, S. Xiao, Y. Chen, Y. Li, D. Jin, S. Uhlig

IEEE Transactions on Network and Service Management (TNSM), 2018.

Measurement-driven modeling for connection density and traffic distribution in large-scale urban mobile networks

Jingtao Ding, R. Xu, Y. Li, P. Hui, D. Jin

IEEE Transactions on Mobile Computing (TMC), 2017.

Time dependent pricing for large-scale mobile networks of urban environment: Feasibility and adaptability

Jingtao Ding, Y. Li, P. Zhang, D. Jin

IEEE Transactions on Services Computing (TSC), 2017.

Measurement-driven capability modeling for mobile network in large-scale urban environment

Jingtao Ding, X. Liu, Y. Li, D. Wu, D. Jin, S. Chen

IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 2016.

Characterizing the spatio-temporal inhomogeneity of mobile traffic in large-scale cellular data networks

H. Wang, Jingtao Ding, Y. Li, P. Hui, J. Yuan, D. Jin

International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking (HotPOST), 2015, Best Paper Award.

Characterizing the phenomenon of traffic tide for large-scale mobile cellular data networks

Jingtao Ding, Y. Li, D. Jin

IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2015.

Biography

Research Interests

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Conference (Senior) Program Committee Member

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Invited Journal Reviewer