Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, an IEEE Distinguished Lecturer, currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is also the Associate Dean (Research and Innovation) for the Faculty of Engineering at VUW.
His research is mainly focused on artificial intelligence (AI), machine learning and big data, particularly in evolutionary computation and learning, feature selection/construction and big dimensionality reduction, computer vision and image processing, job shop scheduling nd resource allocation, multi-objective optimisation, classification with unbalanced data and missing data, and evolutionary deep learning and transfer learning. Prof Zhang has published over 600 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over ten international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Emergent Topics in Computational Intelligence, ACM Transactions on Evolutionary Learning and Optimisation, the Evolutionary Computation Journal (MIT Press), Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, Natural Computing, and Engineering Applications of Artificial Intelligence. He has been involving major AI and EC conferences such as GECCO, IEEE CEC, EvoStar, IJCAI, AAAI, PRICAI, PAKDD, AusAI, IEEE SSCI and SEAL as a Chair. He has also been serving as a steering committee member and a program committee member for over 100 international conferences. Since 2007, he has been listed as one of the top ten (currently No. 4) world genetic programming researchers by the GP bibliography.
Prof Zhang is a past Chair of the IEEE CIS Intelligent Systems Applications, the IEEE CIS Emergent Technologies Technical Committee and the IEEE CIS Evolutionary Computation Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
More information can be seen from his personal website:
Since the 1990s, evolutionary computation techniques have been widely used to solve machine learning tasks. In this talk, I will firstly provide a brief overview of machine learning and evolutionary computation, then focus on the main paradigms of evolutionary machine learning and their success in classification, feature selection, regression, clustering, computer vision and image analysis, scheduling and combinatorial optimisation, and evolutionary deep learning. The main challenges and applications will be discussed. If time allows, I will provide an overview of our research group and discuss potential collaborations.
If you want to join this seminar, please leave your name and email below, you will get the Zoom link once you click submit.