MIN Faculty
Department of Informatics
Knowledge Technology

Junpei Zhong

Since May 2015, I have moved to the University of Plymouth as a postdoctoral researcher.

Contact Info

New Address: A225, Portland Square, Plymouth, Devon, PL4 8AA, United Kingdom
Email: zhong AT junpei.eu
URL: http://www.junpei.eu


Research Interests

My current research encompasses the following interrelated research themes:
    Brain modelling (on the system and cognitive levels), particularly the learning models for sensorimotor system
    Embodiment models based on machine learning methods (connectionist, statistical, among others) on robotic or other adaptive systems
    Psychology, philosophy and their contributions to developmental robotics
My previous research also includes SLAM, and its biological-inspired solutions.




Short Curriculum Vitae

9.2002-7.2006 Bachelor of Engineering, Department of Control Science, South China University of Technology, Guangzhou China
9.2006-7.2007 Department of Control & Mechatronics, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen China
10.2007-10.2009 Master of Philosophy, Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong
7.2010-12.2013 PhD and Research Associate of Knowledge Technology Research Group, Department of Computer Science, University of Hamburg, Germany
1.2014-1.2015 Research Fellow of Adaptive Systems Group, School of Computer Science, University of Hertfordshire, United Kingdom
5.2015-Present Postdoctoral Researcher of Centre for Robotics and Neural Systems, School of Computing, Electronics and Mathematics, University of Plymouth, United Kingdom



  • Artificial Neural Models for Feedback Pathways for Sensorimotor Integration
  • Supervisor: Prof. Stefan Wermter, Doctoral Thesis, Department of Computer Science, University of Hamburg, Germany
  • Utilization and Optimization for Particle Filtering Multi-robot SLAM
  • Supervisor: Dr. Y.F. Fung, MPhil Thesis, Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong



    • Zhong, J.P. and Fung, Y.F., A Biological Inspired Improvement Strategy for Particle Filters. Proceedings, IEEE 2009 International Conference on Industrial Technology (ICIT 09), Australia, pp. 1-6, 10-13 Feb 2009.
    • Zhong, J.P., Fung Y.F. and Dai M.J. Ant Colony Optimization Assisted Particle Filters. International Journal of Control, Automation, and Systems. pp. 519-526, June, 8(3), 2010.
    • Zhong, J.P., Weber, C. and Wermter S., Robot Trajectory Prediction and Recognition based on a Computational Mirror Neurons Model. In Honkela, T., Duch, W., Girolami, M., Kaski, S., editors, Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011), Part II, pp. 333-340, Espoo, Finland, June 2011.
    • Zhong, J., Weber, C. Wermter, S. Restricted Boltzmann Machine with Transformation Units in a Mirror Neuron System Architecture. In Narioka, K., Nagai, Y., Asada, M., Ishiguro, H., editors, Proceedings of the IROS2011 Workshop on Cognitive Neuroscience Robotics (CNR), pp. 23-28, San Francisco, CA, USA, September 2011.
    • Zhong, J., Fung, Y.F. Case Study and Proofs of Ant Colony Optimisation Improved Particle Filter Algorithm. IET Control Theory and Applications. pp. 689-697, 6(5), 2012.
    • Zhong, J., Weber, C. Wermter, S. Learning Features and Transformations with a Predictive Horizontal Product Model. Proceedings of Sixteenth International Conference on Cognitive and Neural Systems, ICCNS 2012, Boston, USA, 2012.
    • Zhong, J., Weber, C., Wermter, S. Learning Features and Predictive Transformation Encoding Based on a Horizontal Product Model. In Villa, A.E.P., et al., editors, Proceedings of the 22nd International Conference on Artificial Neural Networks (ICANN 2012), Part I, LNCS 7552, pp. 539-546, Springer Heidelberg. Lausanne, CH, September 2012.
    • Zhong, J., Weber, C., Wermter, S. A Predictive Network Architecture for a Robust and Smooth Robot Docking Behavior. Paladyn. Journal of Behavioral Robotics. pp. 172-180, 3(4), 2012
    • Zhong, J., Cangelosi, A., Wermter, S. Towards a Self-organizing Pre-symbolic Neural Model Representing Sensorimotor Primitives. Frontiers in Behavioral Neuroscience, 8, pp. 1-11, 10.3389/fnbeh.2014.00022, 2014
    • Zhong, J. and Canamero, L. From Continuous Affective Space to Continuous Expression Space: Non-verbal Behaviour Recognition and Generation (Accepted). The Fourth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2014)