Assistant Prof. @ PKU
Currently, I am an assistant professor at Peking University. Before, I was a research associate in the Hong Kong University of Science and Technology, working with Prof. Qiang Yang and Prof. Xiaojuan Ma from 2016 to 2018. In May 2016, I obtained Ph.D. at Institut Mines-Télécom (IMT) and Université Pierre et Marie CURIE (UPMC), Paris, under the supervision of Prof. Daqing ZHANG and Prof. Abdallah MHAMED. I got my B.S. (2009) and M.S.(2012) in computer science from Peking University, Beijing, under the supervision of Prof. Bing XIE. My Research interests include spatio-temporal computing, with special focus in crowd sensing & intelligence, security & privacy, and spatio-temporal transfer learning.
Email: leyewang@pku.edu.cn
Urban Computing ToolBox is a package providing spatial-temporal predicting models. It contains both conventional models and state-of-art deep learning models. Besides, benchmark datasets built from open data are included. More details in:
L. Wang, D. Chai, X. Liu, L. Chen, & K. Chen. “Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework”. IEEE Trans. Knowledge and Data Engineering (2023).
陈李越，柴迪，王乐业. “UCTB: 时空人群流动预测工具箱”. 计算机科学与探索 (2021).
L. Chen, D. Chai, L. Wang. “UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow Prediction”. arXiv (2023).
Based on UCTB, we have published several works on spatio-temporal prediction, including:
L. Chen, J. Fang, T. Liu, S. Cao, L. Wang, “A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units”. ICDE 2024.
L. Chen, J. Fang, Z. Yu, Y. Tong, S. Cao, L. Wang, “A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management”. KDD 2023.
FedEval is a federated learning benchmark system with a comprehensive evaluation model, ACTPR, inside. The ACTPR model is the core of our benchmarking system. It defines five evaluation metrics for the FL systems: Accuracy, Communication, Time consumption, Privacy and Robustness.
D. Chai, L. Wang, K. Chen, & Q. Yang. “FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning”. arXiv (2022).
D. Chai, L. Wang, L. Yang, J. Zhang, K. Chen, Q. Yang. “A Survey for Federated Learning Evaluations: Goals and Measures”. IEEE Trans. Knowledge and Data Engineering, early access (2024).
For full publication list, please visit my Google Scholar or DBLP page.
(new) D. Chai, L. Wang, L. Yang, J. Zhang, K. Chen, Q. Yang. “A Survey for Federated Learning Evaluations: Goals and Measures”. IEEE Trans. Knowledge and Data Engineering, early access (2024). [pdf]
(new) J. Zhu, L. Wang, X. Han, A. Liu, T. Xie. “Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks toward AI Software Deployment”. IEEE Trans. Software Engineering, 50(3): 376-390 (2024). [pdf]
(new) X. Han, Y. Yang, L. Wang, J. Wu, “Privacy-Preserving Network Embedding against Private Link Inference Attacks”. IEEE Trans. Dependable and Secure Computing, 21(2): 847-859 (2024).
R. Ding, Y. Zhou, J. Xu, Y. Xie, Q. Liang, H. Ren, Y. Wang, Y. Chen, L. Wang, M. Huang, “Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble”. IEEE Journal of Biomedical and Health Informatics, 27(6): 3049-3060 (2023).
X. Han, L. Wang, W. Fan. “Cost-effective social media influencer marketing”. INFORMS Journal on Computing, 35(1): 138-157 (2023).
L. Wang, D. Chai, X. Liu, L. Chen, K. Chen. “Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework”. IEEE Trans. Knowledge and Data Engineering, 35(4): 3870-3884 (2023). [pdf]
X. Han, L. Wang, W. Fan, “Is Hidden Safe? Location Protection against Machine-Learning Prediction Attacks in Social Networks”. MIS Quarterly, 45(2) (2021).
L. Wang, D. Yang, X. Han, D. Zhang, X. Ma, “Mobile Crowdsourcing Task Allocation with Differential-and-Distortion Geo-Obfuscation”. IEEE Trans. Dependable and Secure Computing, 18(2): 967-981 (2021).
L. Wang, D. Zhang, D. Yang, B. Y. Lim, X. Han, X. Ma, “Sparse Mobile Crowdsensing with Differential and Distortion Location Privacy”. IEEE Trans. Information Forensics and Security, 15(1): 2735-2749 (2020).
D. Chai, L. Wang, K. Chen, Q. Yang, “Secure Federated Matrix Factorization”. IEEE Intelligent Systems, 36(5): 11-20 (2021). [200+ citations]
L. Wang, D. Zhang, Y. Wang, C. Chen, X. Han, A. Mhamed. “Sparse Mobile Crowdsensing: Challenges and Opportunities”. IEEE Comm. Mag. 54(7): 161-167 (2016). [200+ citations]
(new) L. Chen, J. Fang, T. Liu, S. Cao, L. Wang, “A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units”. ICDE 2024.
(new) Y. Wu, L. Wang, X. Han, H. Ye, “Graph Contrastive Learning with Cohesive Subgraph Awareness”. WWW 2024.
L. Chen, L. Wang, J. Xu, S. Chen, W. Wang, W. Zhao, Q. Li, L. Wang, “Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer”. CIKM 2023.
L. Chen, J. Fang, Z. Yu, Y. Tong, S. Cao, L. Wang, “A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management”. KDD 2023.
R. Ding, X. Han, L. Wang, “A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks”. ACL Findings 2023.
C. Huang, L. Wang, X. Han, “Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks”. WWW 2023.
R. Ding, F. Rong, X. Han, L. Wang, “Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals”. WWW 2023.
J. Zhu, H. Huang, B. Li, L. Wang, “E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation”. ICLR 2023.
D. Chai, L. Wang, J. Zhang, L. Yang, S. Cai, K. Chen, Q. Yang, “Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data”. KDD 2022.
J. Zhu, L. Wang, X. Han, “Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment”. ASE 2022.
L. Wang, X. Geng, X. Ma, F. Liu, Q. Yang. “Cross-City Transfer Learning for Deep Spatiotemporal Prediction”. IJCAI 2019. [150+ citations]
X. Geng, Y. Li, L. Wang, L. Zhang, J. Ye, Y. Liu, Q. Yang. “Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting”. AAAI 2019.[700+ citations][Most Influential Papers in AAAI 2019]
D. Chai, L. Wang, Q. Yang. “Bike Flow Prediction with Multi-Graph Convolutional Networks”. SIGSPATIAL/GIS 2018: 397-400. [200+ citations]
L. Wang, D. Yang, X. Han, T. Wang, D. Zhang, X. Ma. “Location Privacy-Preserving Task Allocation in Mobile Crowdsensing with Differential Geo-Obfuscation”. WWW 2017. [150+ citations]
L. Wang, D. Zhang, A. Pathak, C. Chen, H. Xiong, D. Yang, Y. Wang. “CCS-TA: Quality-Guaranteed Online Task Allocation in Compressive Crowdsensing”. UbiComp 2015. [150+ citations]