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Associate Professor Kai Qin

Associate Professor

Biography

Kai Qin is an Associate Professor in Data Science and AI. He is the Director of Swinburne Intelligent Data Analytics Lab in Digital Research Innovation Capability Platform, Program Lead for "Data Analysis" in Data Science Research Institute, and Theme Lead for "Predictive Intelligence, Traffic Forecasting, AI and Data Analytics" in Smart Cities Research Institute. He was also the Foundation Director of the Master (and Graduate Certificate) of Data Science Course.

A/Prof Qin received the B.Eng. degree from Southeast University, Nanjing, China, in 2001, and the Ph.D. degree from Nanyang Technology University, Singapore, in 2007. From 2007 to 2017, he was with the University of Waterloo, Waterloo, ON, Canada, with INRIA, Grenoble-Rhône-Alpes, France, and with RMIT University, Melbourne, VIC, Australia. In 2017, he joined Swinburne as an Associate Professor.

A/Prof Qin's primary research field is Computational Intelligence (CI) which studies biologically and linguistically motivated computational paradigms and approaches to solve complex real-world problems to which traditional methods become ineffective or infeasible. CI is a subfield of AI, consisting of three major pillars, i.e., Neural Networks, Evolutionary Computation and Fuzzy Systems. Notably, Neural Networks are the scaffold of Deep Learning which is a subfield of Machine Learning and the mainstay of modern AI techniques, driving the reviving and booming of AI in recent years.

A/Prof Qin's major research interests include Machine Learning, Neural Networks, Evolutionary Computation, Computer Vision, Remote Sensing, Services Computing and Pervasive Computing. So far his works have received 11k+ Google citations. He was the recipient of 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 in 2014. He is now the Vice-Chairs of the IEEE Neural Networks Technical Committee and the IEEE Emergent Technologies Task Forces on “Collaborative Learning and Optimisation” and “Multitask Learning and Multitask Optimisation”. He is the General Co-Chair of the 2022 IEEE International Joint Conference on Neural Networks (IJCNN 2022).

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

Awards

  • 2020, Swinburne, FSET Research Excellent Award, Swinburne University of Technology
  • 2019, Swinburne, Outstanding Academic Teachers for 2019, Swinburne University of Technology
  • 2019, Swinburne, FSET Research Collaboration Across Non-traditional Boundaries Award, Swinburne University of Technology
  • 2016, Other, Certificate of Recognition for an Outstanding Good Teaching Scale, RMIT University
  • 2015, Other, RMIT International Research Exchange Fellowship, RMIT University
  • 2014, International, Overall Best Paper Award, The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014), Singapore, November 10-12, 2014
  • 2012, International, Outstanding Paper Award, IEEE Transactions on Evolutionary Computation
  • 2012, International, Best Paper Nominee, The 2012 Genetic and Evolutionary Computation Conference (GECCO 2012) Philadelphia, USA, July 7-12, 2012
  • 2011, Other, RMIT Vice-Chancellor’s Research Fellowship, RMIT University

Further information

  • https://scholar.google.com/citations?user=d1NlnjQAAAAJ&hl=en

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

  • 2020: Data-driven Traffic Analytics for Incident Analysis and Management *; ARC Linkage Projects Scheme
  • 2020: Deep Learning for Interpreting Prostate PET/CT Images *; Peter MacCallum Cancer Institute
  • 2020: Enhanced State Awareness of Naval Platforms through Data Analysis *; APR Internship Program
  • 2020: Next-generation Intelligent Explorations of Geo-located Data *; ARC Discovery Projects Scheme
  • 2020: Validate and improve Breast Cancer AI approach *; Medical Device Partnering Program
  • 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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