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Professor Kai Qin



Kai Qin is a Professor in Data Science and AI. He is the Director of Intelligent Data Analytics Lab in Swinburne Digital Research Innovation Capability Platform, the Deputy Director and the Program Leader of Remote Sensing in Swinburne Space Technology and Industry Institute, and the Program Leader of Data Analysis in Swinburne Data Science Research Institute. He was also the Founding Director (2018-2020) of the Master (and Graduate Certificate) of Data Science Course.

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. He joined Swinburne in 2017 as an Associate Professor and got promoted to Professor in 2021.

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.

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 12k+ 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 Chair of the IEEE Neural Networks Technical Committee and the Vice-Chair of 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.


Available to supervise honours students.

Fields of Research

  • Neural Networks - 461104
  • Evolutionary Computation - 460203
  • Fuzzy Computation - 460204
  • Image Processing - 460306
  • Data Mining And Knowledge Discovery - 460502
  • Neural Networks - 461104
  • Pattern Recognition - 460308


  • 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



Also published as: Qin, Kai; Qin, K.; Qin, A. K.; Qin, A. Kai; Qin, Alex K.
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

  • 2024: EM Verification of Onboard Smoke Detection Model and Algorithms *; SmartSat CRC Fund Scheme
  • 2023: Revealing the Unseen Universe with Gravitational Lensing *; ARC Discovery Projects Scheme
  • 2023: Robust Predictive AI: Advanced Satellite Hyperspectral Band Registration for Reliable Natural Disaster Event Prediction *; SmartSat CRC Fund Scheme
  • 2022: AI and Remote Sensing for Water Leak Detection *; EY Business Solutions Fund Scheme
  • 2022: Energy-efficient on-board AI for early fire-smoke detection *; SmartSat CRC Fund Scheme
  • 2021: AI Architectures for On-board Processing *; SmartSat CRC Fund Scheme
  • 2021: Mechanical Properties of Additively Manufactured Metamaterials using Machine Learning *; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  • 2021: Satellite imagery to support management of remote operations *; EY Business Solutions Fund Scheme
  • 2021: Scaling of detection devices through automation for rapid commercialistion *; mDetect Pty Ltd
  • 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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