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Introduction

I am an academic staff member in School of Computer ScienceThe University of Manchester (UoM), where I direct the Machine Learning and Perception (MLP@UoM) Lab affliated with Machine Learning and Optimization Research Group. My Research Interests mainly lie in machine learning, pattern recognition, machine perception, computational cognitive modelling and their applications in intelligent system development. I have been in the current post since 2003. I was with Peking University, The University of Birmingham, The Ohio State University, Kyushu Institute of Technology and Tsinghua University. I was a visiting senior researcher at Microsoft Research Asia and a visiting professor at The Hong Kong Polytechnic University. At present, I am a senior member of IEEE, a member of IEEE Computational Intelligence Society, a member of International Neural Network Society and a member of British Computer Socity. I have served as an associate editor or a member of editorial board for a number of academic journals including Neural Networks: An Official Journal of JNNS, ENNS and JNNS, and IEEE Transactions on Neural Networks. For all my academic activities, see my Academic Activity page for details.

I am keen having self-motivated PhD students join my MLP@UoM Lab.  For prospective applicants who have research interests similar to mine (for details, see my Research Interest and Publication pages) , they are strongly suggested visiting my Research Student page first prior to contacting me, where you should be able to find a proper academic inquiry procedure and important information including my PhD Research Project Suggestions, our PhD application procedure and the funding information for UK/EU and non-EU applicants. For a junior researcher who recently received a PhD in a discipline relating to my research interests, they can directly contact me for the further information if they want to visit my MLP@UoM Lab.


Spotlighted Researches in MLP@UoM (2015-Present)

Our work on Feature Importance Ranking for Deep Learning has been accepted for presentation in NeurIPS 2020. Its pre-print is availalbe from HERE.

Our project on Zero-shot Visual Recognition has yielded a number of publications. The work on Bidirectional Latent Embedding Learning has been published in International Journal of Computer Vision (the pre-print is availalbe from HERE, and the source code in Matlab is available on our Project Website). The paper on Multi-label Zero-shot Human Action Recognition has been published in Neural Networks (the pre-print is available from HERE, and the source code in Matlab is available on our Project Website). The work on Alternative Semantic Representations for Zero-shot Human Action Recognition was presented in ECML'17, and the paper and source code are available on our Project Website.

Our theoretical work on Feature Distribution Learning has yielded two publications. The work on Theoretical Aspects of Sparse Filtering has been published in Neural Networks and its pre-print is availalbe from HERE. The Covariant Adaptation via Sparse Filtering paper is available from HERE.

Our work on Learning-based Video Game Development has made substantial progresses in procedural content generation, serious education games, fast skill capture and learnable agents. Unlike most of existing works, we developed and applied machine learning techniques to address those challenges in video game development. The researches have led to five PhD theses and a number of papers published in IEEE Trans. Games, Entertainment Computing and IEEE Int. Conf. Games. An invited paper overviewing the main progresses is available from HERE, and the pre-prints of those papers are available on Publications in Learning-based Video Game Development.

Our work on Learning Contextualized Semantics from Co-occuring Descriptive Terms has been published in Neural Networks. The pre-print is availalbe from HERE  and the source code in Matlab is available upon request and agreement. This work has led to non-trivial applications, e.g., Multi-label Zero-shot Learning (see our Technical Report for details) and Music Information Retrieval (published in ACM Transactions on Intelligent Systems and Technology and the pre-print is availalbe from HERE).

Our work on Spatial Cognition Modelling has made substatial progresses, which leads to a number of papers published in PLoS One, Neural Networks, Cognitive System Research and Biologically Inspired Cognitive Architectures etc. Those pre-prints are available on Spatial Cognition Modelling Publications.


 

This page was last updated at 11:48am, October 20th, 2020.
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