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Prof. Haipeng Zhang / 张海鹏 助理教授、研究员

Tel:  (021) 20684458
Office: Room 1C-303C, SIST Building, No.393 Huaxia Middle Road, Pudong Area
Major: CS


  • Data Mining, in particular mining e-business, social media, smart phone and financial data


Dr. Haipeng Zhang received his B.E. degree in Software Engineering from Nanjing University in 2009. He was an exchange student at HKUST in Fall 2007. He received his PhD in Computer Science from Indiana University under the supervision of Prof. David J. Crandall in 2014. He publishes in venues including WWW and WSDM and his work received media coverage from New Scientist magazine and the Communications of the ACM website. From 2010 to 2013, he did research internships at National Institute of Informatics (Tokyo), eBay Research Labs (San Jose, CA), Microsoft Research (Cambridge, UK) and Samsung Research North America (San Jose, CA). From 2014 to 2018, he worked at IBM Research and China Financial Futures Exchange on Data Science and Fintech. In August 2018, he joined ShanghaiTech as a tenure-track assistant professor.


1. Zhang, H., Korayem, M., Crandall, D. J., and LeBuhn, G. Mining photo-sharing websites to study ecological phenomena. In Proceedings of the 21st international conference on World Wide Web (2012), ACM, pp. 749-758. (oral, 12%, CCF A)
2. Zhang, H., Korayem, M., You, E., and Crandall, D. J. Beyond co-occurrence: discovering and visualizing tag relationships from geo-spatial and temporal similarities. In Proceedings of the fifth ACM international conference on Web search and data mining (2012), ACM, pp. 33-42. (plenary talk, 8.3%, CCF B)
3. Zhang, H., Parikh, N., Singh, G., and Sundaresan, N. Chelsea won, and you bought a t-shirt: Characterizing the interplay between twitter and e-commerce. In Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining (2013), ACM, pp. 829-836. (oral, Best Paper Award)
4. Zhang, H., Yan, Z., Yang, J., Tapia, E. M., and Crandall, D. J. Mfingerprint: Privacy-preserving user modeling with multimodal mobile device footprints. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (2014), Springer, pp. 195-203. (oral, 24%)
5. Lee, S., Zhang, H., and Crandall, D. J. Predicting geo-informative attributes in large-scale image collections using convolutional neural networks. In 2015 IEEE Winter Conference on Applications of Computer Vision (2015), IEEE, pp. 550-557. (oral, 36.7%)