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Associate Professor Kai Qin

Associate Professor

Biography

Kai Qin is an Associate Professor in the Department of Computer Science and Software Engineering and also a core member of the Data Science Research Institute at Swinburne. He is currently leading the machine learning research group based in the Data Science Research Institute.

Dr Qin received the B.Eng. degree at Southeast University (China) in 2001 and the Ph.D. degree at Nanyang Technology University (Singapore) in 2007. From 2007 to 2012, he had worked first at the University of Waterloo (Canada) and then at INRIA (France). Since 2013, he was a Vice-Chancellor's Research Fellow, Lecturer and Senior Lecturer at RMIT. In February 2017, he joined Swinburne University of Technology as an Associate Professor.

Dr Qin’s major research interests include machine learning, deep learning, evolutionary computation, computer vision, GPU computing, and services computing. His works have received over 7400 citations in Google Scholar. He won the 2012 IEEE Transactions on Evolutionary Computation Outstanding Paper Award and the Overall Best Paper Award at the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014). One of his conference papers was nominated for the best paper award at the 2012 Genetic and Evolutionary Computation Conference (GECCO 2012). As an IEEE senior member, he is chairing the IEEE Emergent Technologies Task Force on “Collaborative Learning and Optimisation”, promoting international research efforts on the synergy of machine learning and optimisation.

PhD candidate and honours supervision

Higher degrees by research

Accredited to supervise Masters & Doctoral students as Principal Supervisor.

Honours

Available to supervise honours students.

Fields of Research

  • Neural, Evolutionary And Fuzzy Computation - 080108
  • Pattern Recognition And Data Mining - 080109
  • Image Processing - 080106

Publications

Also published as: Qin, Kai; Qin, K.; Qin, A. K.; Qin, A. Kai
This publication listing is provided by Swinburne Research Bank. If you are the owner of this profile, you can update your publications using our online form.

Recent research grants awarded

  • 2018: Bus replacement services for rail passenger service disruptions *; iMOVECRC
  • 2018: Identifying technological trajectories using machine learning algorithms *; ARC Linkage Projects Scheme
  • 2016: Long-term Cloud Service Composition *; ARC Discovery Projects Scheme

* Chief Investigator


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