Professor Kai Qin

Biography

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).