Kaiqi Zhao
Lecturer
School of Computer Science
38 Princes Street, Auckland 1010
I am currently a Lecturer with the School of Computer Science at the University of Auckland and a member of Machine Learning Group @ UoA. I have a broad research interests in machine learning and data mining. Specifically, my recent research focuses on spatio-temporal data mining, text mining and recommender systems. I am interested in building machine learning models to address practical problems such as geo-topic mining, GPS mobility modeling and user preference mining.
Prospective graduate student: I am looking for self-motivated and hard-working Honors/Master/PhD students to do exciting research in spatio-temporal data mining, text mining and recommender system. Please email your CV if you are interested!
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.
- 2007 – 2009: B.A. in Japanese Language, School of Foreign Languages, Huazhong University of Science and Technology.
- 2005 – 2009: B.Eng. in Software Engineering, School of Software Engineering, Huazhong University of Science and Technology.
Research
Spatio-temporal data mining
The prevalence of location positioning devices such as GPS have made huge amount of geo-tagged data available. My research on spatio-temporal data mining is to automatically uncover latent patterns from data associated with GPS coordinates over time. This research area links to various of applications including GPS trajectory mining, local event detection, location prediction and traffic prediction.
Text mining
Mining big text data is challenging because of its unstructured nature. In this research area, I focus on developing scalable models for understanding big text data. Specifically, we study the problems of efficient topic mining from tweets; sentiment analysis for online location-based reviews; named entities disambiguation via entity linking; and text summarization with knowledge graphs.
Recommender systems
Recommender system is a common practice to address the information overload problem and is widely applied in many industries such as journalism, e-commerce system and location-based services. My approach is to use advanced machine learning techniques on interesting recommendation problems, such as location-based recommendations, session-based recommendation, long-short term user preference modeling.
Publication
Teaching
- Algorithms for Massive Data (COMPSCI753), Instructor, 2020 S2, UoA
- Data-mining and Machine Learning (COMPSCI760), Instructor, 2020 S2, UoA
- Artificial Intelligence (COMPSCI367), Instructor, 2020 S1, UoA-SWU
- Data-mining and Machine Learning (COMPSCI760), Instructor, 2019 S2, UoA
- Machine Learning (COMPSCI361), Guest Lecturer, 2019 S2, UoA
- Introduction to Database (CZ2007), Teaching Assistant, Fall 2016, NTU
- Database System Principles (CZ4031), Teaching Assistant, Fall 2015, NTU
- Windows Internals (CS490), Teaching Assistant, Fall 2010, SJTU
Professional Services
Program Committee Member:
- ADMA 2017, CIKM 2017, WSDM 2021, PAKDD 2021, IJCAI 2021, KDD 2021
- AAAI since 2020
- SIGIR since 2020
Journal Reviewer:
- TOIS, TKDE, TKDD, IEEE Trans. on Big Data, World Wide Web Journal.
External Reviewer:
- 2018: KDD, SIGMOD, ICDE, WWW
- 2017: WWW, DASFAA, WSDM, AAAI
- 2016: WWW, AAAI, CIKM, ICDM
- 2015: WWW, ACL, Trans. Big Data
- 2014: KDD, TKDE, CIKM, SDM, ICWSM, COLING
- Others: WWW 2013, EMNLP 2013, CIKM 2012, WWW 2011, ECML 2011