News

• 2019.01 Our paper entitled 'Ridesharing Car Detection by Transfer Learning' is accepted by Artificial Intelligence.

• 2018.10 Our paper entitled 'Smart City Development with Urban Transfer Learning' is accepted by IEEE Computer.

• 2017.11 My online course 'Introduction to Data Analytics' (Chinese: 数据分析师-入门) has achieved 1500+ registered students.

Biography

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. Recently, I am also devoted into a new transfer learning paradigm, urban spatio-temporal transfer learning; please find more information in our vision paper here.

Highlight Talks

• Geographic Differential Privacy in Urban Mobile Crowdsensing, 12/2017. [slides]

• Urban Transfer Learning, 5/2018. [slides] [paper]

Highlight Project Repository (Code & Data)

UCTB (Urban Computing ToolBox)

• Urban Computing ToolBox is a package providing spatial-temporal prediction models. It contains both conventional statistical models and state-of-art deep learning models. Besides, benchmark datasets built from open data are included.

• Wang, L., Chai, D., Liu, X., Chen, L., & Chen, K. (2020). Exploring the Generalizability of Spatio-Temporal Crowd Flow Prediction: Meta-Modeling and an Analytic Framework. arXiv preprint arXiv:2009.09379. [Arxiv]

Selected Publications

Journal

• L. Wang, B. Guo, Q. Yang. Smart City Development with Urban Transfer Learning. IEEE Computer'18, vol. 51, no. 12, pp. 32-41, 2018. [Arxiv version]

• L. Wang, D. Zhang, D. Yang, A. Pathak, C. Chen, X. Han, H. Xiong, Y. Wang. SPACE-TA: Cost-Effective Task Allocation Exploiting Intradata and Interdata Correlations in Sparse Crowdsensing. TIST'18: ACM Transactions on Intelligent Systems and Technology, vol. 9, no. 2, pp. 20:1-20:28, 2018. (extension of UbiComp'15 paper) [ACM] [pdf]

• L. Wang, D. Zhang, H. Xiong, J. P. Gibson, C. Chen, B. Xie. ecoSense: Minimize Participants’ Total 3G Data Cost in Mobile Crowdsensing Using Opportunistic Relays. TSMC'17: IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 6, pp. 965-978, 2017. [IEEE] [pdf]

• L. Wang, D. Zhang, Y. Wang, C. Chen, X. Han, A. Mhamed. Sparse Mobile Crowdsensing: Challenges and Opportunities. COMMAG'16: IEEE Communications Magazine, vol. 54, no. 7, pp. 161-167, 2016. [IEEE] [pdf]

• L. Wang, D. Zhang, Z. Yan, H. Xiong, B. Xie. effSense: A Novel Mobile Crowdsensing Framework for Energy-Efficient and Cost-Effective Data Uploading. TSMC'15: IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 12, pp. 1549-1563, 2015. [IEEE] [pdf] [slides]

Conference

• 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'19: AAAI Conference on Artificial Intelligence, Jan. 2019, Hawaii, USA.

• L. Wang, G. Qin, D. Yang, X. Han, X. Ma. Geographic Differential Privacy for Mobile Crowd Coverage Maximization. AAAI'18: AAAI Conference on Artificial Intelligence, Feb. 2018, New Orleans, USA. [Arxiv]

• L. Wang, D. Yang, X. Han, T. Wang, D. Zhang, X. Ma. Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation. WWW'17: International World Wide Web Conference, Apr. 2017, Perth, Australia, pp. 627-636. [ACM] [pdf] [slides]

• L. Wang, D. Zhang, D. Yang, B. Y. Lim, X. Ma. Differential Location Privacy for Sparse Mobile Crowdsensing. ICDM'16: IEEE International Conference on Data Mining, Dec. 2016, Barcelona, Spain, pp. 1257-1262. [IEEE] [pdf] [slides]

• 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'15: ACM International Joint Conference on Pervasive and Ubiquitous Computing, Sept. 2015, Osaka, Japan, pp. 683-694. [ACM] [pdf] [slides]

For a full list of papers, please refer to DBLP or Google Scholar.

PhD Thesis

• Facilitating Mobile Crowdsensing from both Organizers’ and Participants’ Perspectives. May 2016, France. [pdf] [slides]

Invited Talks

• 2018 "Crowdsensing + AI", Nanyang Technological University, Singapore, Feb. 2018

• 2017 "Geographic Differential Privacy in Urban Mobile Crowdsensing", CCF Forum on Smart Sensing and Urban Computing, Fuzhou University, China, Oct. 2017 [slides]

• 2017 "Urban Trajectory Mining", Tongji Automotive Innovation Forum, Tongji University, China, May 2017 [slides]

Posts

• Big Data on Socio-Economic Factors: a summary of interesting research works on big data and socio-economic issues.

Links

• Prof. Daqing Zhang (Chair Professor @ Peking University, China)

• Prof. Qiang Yang (Chair Professor @ HKUST, HK SAR, China)

• Prof. Xiaojuan Ma (Assistant Professor @ HKUST, HK SAR, China)

• Prof. Chao Chen (Associate Professor @ Chongqing University, China)

• Dr. Dingqi Yang (Research Fellow @ University of Fribourg, Switzerland)

• Dr. Haoyi Xiong (Senior Research Scientist @ Baidu, China)

• Prof. Longbiao Chen (Assistant Professor @ Xiamen University, China)

• Prof. Jiangtao Wang (Assistant Professor @ Peking University, China)

• Prof. Xiao Han (Assistant Professor @ Shanghai University of Finance and Economics, China)