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Dr Kaiqi Zhao

Biography

  • 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

Recommendations

  • 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)

As of 29 October 2020 there will be no automatic updating of 'selected publications and creative works' from Research Outputs. Please continue to keep your Research Outputs profile up to date.
  • 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

SCIENCE CENTRE 303S - Bldg 303S
Level 4, Room 492
38 PRINCES ST
AUCKLAND CENTRAL
AUCKLAND 1010
New Zealand

Web links