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Dr Kaiqi Zhao


  • 2019 - now:  Lecturer, School of Computer Science, University of Auckland.
  • 2018 - 2019: Research Fellow, Singtel Cognitive and Aritificial Intelligence Lab for Enterprise, NTU.
  • 2013 - 2018: Ph.D. in Computer Science, School of Computer Science & Engineering, Nanyang Technological University. Supervisor: Dr. Gao CONG.
  • 2011 - 2012: Intern, Web Search & Mining Group, Microsoft Research Asia. Mentor: Dr. Haixun Wang.
  • 2010 - 2013: M.Eng. in Computer Engineering, Department of Computer Science, Shanghai Jiao Tong University. Supervisor: Dr. Kenny Q. Zhu.
  • 2005 - 2009: B.Eng. in Software Engineering, School of Software Engineering, Huazhong University of Science and Technology.

Research | Current

My current research focuses on data mining and machine learning. Specificially, it covers the following main areas:

Geospatial data mining:

  • Geospatial region analysis
  • GPS trajectory mining
  • User mobility and activity modeling


  • Context-aware recommendations
  • Long-short term user interests integration

Knowledge graph

  • Domain-specific knowlege graph construction
  • Knowledge graph reasoning
  • Knowledge graph applications (e.g., chatbots)

Teaching | Current

COMPSCI760 - Data-mining and Machine Learning

COMPSCI367 - Artificial Intelligence

Areas of expertise

  • Spatio-temporal data management and mining
  • Text mining
  • Knowledge engineering / Knowledge graphs
  • Recommender systems

Selected publications and creative works (Research Outputs)

  • Zhao, K., Cong, G., Chin, J.-Y., & Wen, R. (2019). Exploring market competition over topics in spatio-temporal document collections. VLDB JOURNAL, 28 (1), 123-145. 10.1007/s00778-018-0522-9
  • Chin, J. Y., Zhao, K., Joty, S., & Cong, G. (2018). ANR: Aspect-based Neural Recommender. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, 147-156. New York, NY, USA: ACM. 10.1145/3269206.3271810
  • Li, X., Zhao, K., Cong, G., Jensen, C. S., & Wei, W. (2018). Deep Representation Learning for Trajectory Similarity Computation. 2018 IEEE 34th International Conference on Data Engineering (ICDE), 617-628. Paris, France: IEEE. 10.1109/ICDE.2018.00062
  • Liu, Y., Zhao, K., & Cong, G. (2018). Efficient Similar Region Search with Deep Metric Learning. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery& Data Mining, KDD 2018, London, UK, August 19-23, 2018, 1850-1859. New York, NY, USA: ACM. 10.1145/3219819.3220031
  • Zhao, K., Cong, G., & Sun, A. (2016). Annotating Points of Interest with Geo-tagged Tweets. Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016, 417-426. New York, NY, USA: ACM. 10.1145/2983323.2983850
  • Gong, Y., Zhao, K., & Zhu, K. Q. (2016). Representing Verbs as Argument Concepts. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2615-2621. Phoenix, Arizona, USA.
  • Zhao, K., Chen, L., & Cong, G. (2016). Topic Exploration in Spatio-Temporal Document Collections. Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26 - July 01, 2016, 985-998. New York, NY, USA: ACM. 10.1145/2882903.2882921
  • Zhao, K., Cong, G., Yuan, Q., & Zhu, K. Q. (2015). SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews. 31st IEEE International Conference on Data Engineering, ICDE 2015, Seoul, South Korea, April 13-17, 2015, 675-686. Seoul, South Korea. 10.1109/ICDE.2015.7113324

Contact details

Primary office location

Level 4, Room 492
New Zealand

Web links